Search

We study the three-dimensional Carrollian field theory on the Rindler horizon which is dual to a bulk massless scalar field theory in the four-dimensional Rindler wedge. The Carrollian field theory could be mapped to a two-dimensional Euclidean field theory in the transverse plane by a Fourier transform. After defining the incoming and outgoing states at the future and past Rindler horizon, respectively, we construct the boundary-to-boundary and bulk-to-boundary propagators that are consistent with the bulk Green's function in the literature. We investigate the tree-level Carrollian amplitudes up to four points. The tree-level four-point Carrollian amplitude in $\Phi^4$ theory has the same structure as the one-loop triangle Feynman integral in the Lee-Pomeransky representation with complex powers in the propagators and spacetime dimension. Moreover, the four-point Carrollian amplitude with a zero energy state inserted at infinity in $\Phi^4$ theory is proportional to the three-point Carrollian amplitude in $\Phi^3$ theory., Comment: 78 pages, 14 figures, 8 tables

Employing aberration-corrected scanning transmission electron microscopy (STEM), we meticulously investigated the intrinsic chemical heterogeneity of Fe1+yTe, Fe1+yTe0.8Se0.2, and Fe1+yTe0.5Se0.5. Comprehensive analysis reveals the presence of interstitial iron atoms (Feint) across all samples, pre-dominantly occupying the 2c site of the P4/nmm space group. Moreover, a superstructure phase characterized by a wave vector q = 2/5a + 1/2c, originating from the ordering of Feint, is distinctly observable in the parent compound Fe1+yTe. In this scenario, the Feint atoms interact with adjacent Fe atoms, forming iron polycomplex and leading to an evident distortion of the FeTe4 tetrahedral. Experimental results further demonstrate effective suppression of Feint concentration and ordering through appropriate Se substitution; notably, Fe1+yTe0.5Se0.5 manifests the lowest concentration of Feint atoms. Our findings additionally indicate that Se substitution is random, and nanoscale phase separation induced by Te/Se chemical heterogeneity is commonly observed within Fe1+yTe1-xSex crystals., Comment: 14 pages, 5 figures

The rapid development of large language models has brought many new smart applications, especially the excellent multimodal human-computer interaction in GPT-4o has brought impressive experience to users. In this background, researchers have proposed many multimodal LLMs that can achieve speech-to-speech dialogue recently. In this paper, we propose a speech-text multimodal LLM architecture called Freeze-Omni. Our main contribution is the speech input and output modalities can connected to the LLM while keeping the LLM frozen throughout the training process. We designed 3-stage training strategies both for the modeling of speech input and output, enabling Freeze-Omni to obtain speech-to-speech dialogue ability using text-speech paired data (such as ASR and TTS data) and only 60,000 multi-round text Q&A data on 8 GPUs. Moreover, we can effectively ensure that the intelligence of the Freeze-Omni in the speech modality is at the same level compared with that in the text modality of its backbone LLM, while the end-to-end latency of the spoken response achieves a low level. In addition, we also designed a method to achieve duplex dialogue ability through multi-task training, making Freeze-Omni have a more natural style of dialogue ability between the users. Freeze-Omni mainly provides a possibility for researchers to conduct multimodal LLM under the condition of a frozen LLM, avoiding various impacts caused by the catastrophic forgetting of LLM caused by fewer data and training resources., Comment: Project Page: https://freeze-omni.github.io/

The double neutron star PSR J1846-0513 is discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in Commensal Radio Astronomy FAST Survey. The pulsar is revealed to be harbored in an eccentric orbit with $e=0.208$ and orbital period of 0.613 days. The total mass of the system is constrained to be $2.6287(35)\rm{M}_{\odot}$, with a mass upper limit of $1.3455{\rm~M}_{\odot}$ for the pulsar and a mass lower limit of $1.2845{\rm~M}_{\odot}$ for the companion star. To reproduce its evolution history, we perform a 1D model for the formation of PSR J1846-0513 whose progenitor is assumed to be neutron star - helium (He) star system via MESA code. Since the large eccentricity is widely believed to originate from an asymmetric supernova explosion, we also investigate the dynamical effects of the supernova explosion. Our simulated results show that the progenitor of PSR J1846-0513 could be a binary system consisting of a He star of $3.3-4.0{\rm~M}_\odot$ and a neutron star in a circular orbit with an initial period of $\sim0.5$ days., Comment: 15 pages, 7 figures, Accepted to be published on RAA

We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides an LLM to fulfill an input instruction by simulating multiple experts, aggregating their responses, and selecting the best among individual and aggregated responses. This process is performed in a single chain of thoughts through our seven carefully designed subtasks derived from the Nominal Group Technique (Ven and Delbecq, 1974), a well-established decision-making framework. Our evaluations demonstrate that Multi-expert Prompting significantly outperforms ExpertPrompting and comparable baselines in enhancing the truthfulness, factuality, informativeness, and usefulness of responses while reducing toxicity and hurtfulness. It further achieves state-of-the-art truthfulness by outperforming the best baseline by 8.69% with ChatGPT. Multi-expert Prompting is efficient, explainable, and highly adaptable to diverse scenarios, eliminating the need for manual prompt construction., Comment: EMNLP 2024 Main Conference

The identification of large numbers of localised transient EUV brightenings, with small spatial scales, in the quiet-Sun corona has been one of the key early results from Solar Orbiter. However, much is still unknown about these events. Here, we aim to better understand EUV brightenings by investigating their spatial distributions, specifically whether they occur co-spatial with specific line-of-sight magnetic field topologies in the photospheric network. EUV brightenings are detected using an automated algorithm applied to a high-cadence (3 s) dataset sampled over ~30 min on 8 March 2022 by the Extreme Ultraviolet Imager's 17.4 nm EUV High Resolution Imager. Data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager and Atmospheric Imaging Assembly are used to provide context about the line-of-sight magnetic field and for alignment purposes. We found a total of 5064 EUV brightenings within this dataset that are directly comparable to events reported previously in the literature. These events occurred within around 0.015-0.020 % of pixels for any given frame. We compared eight different thresholds to split the EUV brightenings into four different categories related to the line-of-sight magnetic field. Using our preferred threshold, we found that 627 EUV brightenings (12.4 %) occurred co-spatial with Strong Bipolar configurations and 967 EUV brightenings (19.1 %) occurred in Weak Field regions. Fewer than 10 % of EUV brightenings occurred co-spatial with Unipolar line-of-sight magnetic field no matter what threshold was used. Of the 627 Strong Bipolar EUV Brightenings, 54 were found to occur co-spatial with cancellation whilst 57 occurred co-spatial with emergence. EUV brightenings preferentially occur co-spatial with the strong line-of-sight magnetic field in the photospheric network. They do not, though, predominantly occur co-spatial with (cancelling) bi-poles., Comment: 14 pages, 8 figures, accepted in A&A

Nontrivial spectral properties of non-Hermitian systems can give rise to intriguing effects that lack counterparts in Hermitian systems. For instance, when dynamically varying system parameters along a path enclosing an exceptional point (EP), chiral mode conversion occurs. A recent study [Phys. Rev. Lett. 133, 113802 (2024)] demonstrates the achievability of pure adiabatic state transfer by specifically selecting a trajectory in the system parameter space where the corresponding evolution operator exhibits a real spectrum while winding around an EP. However, considering the impact of nonadiabatic transitions reveals the fragility of intended adiabatic state transfer. In this work, we propose a scheme for achieving robust and rapid adiabatic state transfer in time-modulated two-level non-Hermitian systems by appropriately modulating system Hamiltonian and time-evolution trajectory. Numerical simulations confirm that complete adiabatic transfer can always be achieved even under nonadiabatic conditions after one period for different initialized adiabatic states, and the scheme remains insensitive to moderate fluctuations in control parameters. Therefore, this scheme offers alternative approaches for quantum-state engineering in non-Hermitian systems., Comment: 7 pages, 6 figures

Blockchain interoperability protocols enable cross-chain asset transfers or data retrievals between isolated chains, which are considered as the core infrastructure for Web 3.0 applications such as decentralized finance protocols. However, existing protocols either face severe scalability issues due to high on-chain and off-chain costs, or suffer from trust concerns because of centralized designs. In this paper, we propose \texttt{MAP}, a trustless blockchain interoperability protocol that relays cross-chain transactions across heterogeneous chains with high scalability. First, within \texttt{MAP}, we develop a novel \textit{cross-chain relay} technique, which integrates a unified relay chain architecture and on-chain light clients of different source chains, allowing the retrieval and verification of diverse cross-chain transactions. Furthermore, we reduce cross-chain verification costs by incorporating an optimized zk-based light client scheme that adaptively decouples signature verification overheads from inefficient smart contract execution and offloads them to off-chain provers. For experiments, we conducted the first large-scale evaluation on existing interoperability protocols. With \texttt{MAP}, the required number of on-chain light clients is reduced from $O(N^2)$ to $O(N)$, with around 35\% reduction in on-chain costs and 25\% reduction for off-chain costs when verifying cross-chain transactions. To demonstrate the effectiveness, we deployed \texttt{MAP} in the real world. By 2024, we have supported over six popular public chains, 50 cross-chain applications and relayed over 200K cross-chain transactions worth over 640 million USD. Based on rich practical experiences, we constructed the first real-world cross-chain dataset to further advance blockchain interoperability research.

The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional flexibility for real-time power balance. The forecasting of EV charging demand involves probabilistic modeling of high dimensional time series dynamics across diverse electric vehicle charging stations (EVCSs). This paper studies the forecasting problem of multiple EVCS in a hierarchical probabilistic manner. For each charging station, a deep learning model based on a partial input convex neural network (PICNN) is trained to predict the day-ahead charging demand's conditional distribution, preventing the common quantile crossing problem in traditional quantile regression models. Then, differentiable convex optimization layers (DCLs) are used to reconcile the scenarios sampled from the distributions to yield coherent scenarios that satisfy the hierarchical constraint. It learns a better weight matrix for adjusting the forecasting results of different targets in a machine-learning approach compared to traditional optimization-based hierarchical reconciling methods. Numerical experiments based on real-world EV charging data are conducted to demonstrate the efficacy of the proposed method., Comment: Paper accepted for IEEE Transactions on Industrial Applications. Personal use of this material is permitted. Permission from Elsevier must be obtained for all other uses

Flat bands typically describe energy bands whose energy dispersion is entirely or almost entirely degenerate. One effective method to form flat bands is by constructing Moir\'e superlattices. Recently, there has been a shift in perspective regarding the roles of space (momentum) and time (energy) in a lattice, with the concept of photonic time crystals that has sparked discussions on momentum dispersion such as the presence of a bandgap in momentum. Here we propose a photonic time moir\'e superlattice achieved by overlaying two photonic time crystals with different periods. The resulting momentum bandgap of this superlattice supports isolated momentum bands that are nearly independent of energy, which we refer to as momentum flat bands. Unlike energy flat bands, which have zero group velocity, momentum flat bands exhibit infinitely large group velocity across a broad frequency range. Unlike previous optical media supporting broadband superluminal propagation based on gain, the effective refractive index of the momentum flat bands is real-valued, leading to more stabilized superluminal pulse propagation., Comment: 6 pages, 4 figures

In this paper, we investigate sphericity testing in high-dimensional settings, where existing methods primarily rely on sum-type test procedures that often underperform under sparse alternatives. To address this limitation, we propose two max-type test procedures utilizing the sample covariance matrix and the sample spatial-sign covariance matrix, respectively. Furthermore, we introduce two Cauchy combination test procedures that integrate both sum-type and max-type tests, demonstrating their superiority across a wide range of sparsity levels in the alternative hypothesis. Our simulation studies corroborate these findings, highlighting the enhanced performance of our proposed methodologies in high-dimensional sphericity testi

We investigate the coevolution of metals and dust for 173 galaxies at 4.010 by JWST. Besides, we observe that the metallicity of galaxies at z>8 presents a metal-to-stellar mass ratio larger than a few 10^-3, above a floor. This suggests a very fast rise of metals at high redshift, impacting the tentative detections of population III objects., Comment: This paper is submitted. It contains a main paper with 5 figures and 2 tables, plus supplementary materials

Inverse Optimization (IO) is a framework for learning the unknown objective function of an expert decision-maker from a past dataset. In this paper, we extend the hypothesis class of IO objective functions to a reproducing kernel Hilbert space (RKHS), thereby enhancing feature representation to an infinite-dimensional space. We demonstrate that a variant of the representer theorem holds for a specific training loss, allowing the reformulation of the problem as a finite-dimensional convex optimization program. To address scalability issues commonly associated with kernel methods, we propose the Sequential Selection Optimization (SSO) algorithm to efficiently train the proposed Kernel Inverse Optimization (KIO) model. Finally, we validate the generalization capabilities of the proposed KIO model and the effectiveness of the SSO algorithm through learning-from-demonstration tasks on the MuJoCo benchmark., Comment: NeurIPS 2024

Scaling laws for critical phenomena take pivotal status in almost all branches of physics. However, as scaling laws are commonly guaranteed by the renormalization group theory, systems that violate them have rarely been found. In this letter, we demonstrate that gravitational system can break scaling laws. We derive this result through investigating phase transition and critical phenomenon in a gravitational system with quantum anomaly. For the first time, we outline the key conditions to violate the scaling laws in generic gravitational system viewed from the equation of state $P=P(T,V)$. Our results indicate that quantum effects can magnify the distinctiveness of gravity, which may be significant to understand the microscopic structure of spacetime., Comment: 7 pages, 3 figures

The idea of fractional derivatives has a long history that dates back centuries. Apart from their intriguing mathematical properties, fractional derivatives have been studied widely in physics, for example in quantum mechanics and generally in systems with nonlocal temporal or spatial interactions. However, systematic experiments have been rare due to challenges associated with the physical implementation. Here we report the observation and full characterization of a family of temporal optical solitons that are governed by a nonlinear wave equation with a fractional Laplacian. This equation has solutions with unique properties such as non-exponential tails and a very small time-bandwidth product.

The observed timing data, magnetic tilt angle $\chi$, and age of young pulsars could be used to probe some important issues about neutron star (NS) physics, e.g., the NS internal magnetic field configuration, and the number of precession cycles $\xi$. \textbf{Both} quantities are critical in studying the continuous gravitational wave emission from pulsars, and the latter generally characterizes the mutual interactions between superfluid neutrons and other particles in the NS interior. The timing behavior of pulsars can be influenced by the dipole field evolution, which instead of decaying, may increase with time. An increase in the dipole field may result from the re-emergence of the initial dipole field $B_{\rm d,i}$ that was buried into the NS interior shortly after the birth of the NS. In this work, the field re-emergence scenario $\xi$ and the internal field configuration of several young pulsars, as well as their $B_{\rm d, i}$ are investigated by assuming typical accreted masses $\Delta M$. Moreover, since the Crab pulsar has an exactly known age and its tilt angle change rate can be inferred from observations, we can set stringent constraints on its $\xi$, $B_{\rm d,i}$, and $\Delta M$. Although for other young pulsars without exactly known ages and tilt angle change rates, these quantities cannot be accurately determined, we find that their $\xi$ are generally within $\sim10^4-10^6$, and some of them probably have magnetar-strength $B_{\rm d,i}$. Our work could be important for investigating the transient emissions associated with NSs, the origin of strong magnetic fields of NSs, pulsar population, continuous gravitational wave emission from pulsars, and accretion under extreme conditions in principle.

The initial spin periods of newborn magnetars are \textbf{strongly associated with the origin of their strong magnetic fields, both of which can affect the electromagnetic radiation and gravitational waves (GWs) emitted at their birth.} Combining the upper limit $E_{\rm SNR}\lesssim10^{51}$ erg on the explosion energies of \textbf{the supernova (SN) remnants around slowly-spinning magnetars} with a detailed investigation on the evolution of newborn magnetars, we set constraints on the initial spin periods of magnetars \textbf{born in weak SN explosions}. Depending on the conversion efficiency $\eta$ of the electromagnetic energy of \textbf{these} newborn magnetars into the kinetic energy of SN ejecta, the minimum initial spin periods of \textbf{these} newborn magnetars are $P_{\rm i, min}\simeq 5-6$ ms for an ideal efficiency $\eta=1$, $P_{\rm i, min}\simeq 3-4$ ms for a possible efficiency $\eta=0.4$, and $P_{\rm i, min}\simeq 1-2$ ms for a relatively low efficiency $\eta=0.1$. \textbf{Based on these constraints and adopting reasonable values for the physical parameters of the newborn magnetars, we find that their GW radiation at $\nu_{\rm e,1}=\nu$ may be undetectable by the Einstein Telescope (ET) since the maximum signal-to-noise ratio (${\rm S/N}$) is only 2.41 even the sources are located at a very close distance of 5 Mpc, where $\nu$ are the spin frequencies of the magnetars. At such a distance, the GWs emitted at $\nu_{\rm e,2}=2\nu$ from the newborn magnetars with dipole fields $B_{\rm d}=5\times10^{14}$ and $10^{15}$ G may be detectable by the ET because ${\rm S/N}$ are 10.01 and 19.85, respectively. However, if these newborn magnetars are located at $20$ Mpc away in the Virgo supercluster, no GWs could be detected by the ET due to low ${\rm S/N}$.

The observational data (e.g., the timing data and magnetic tilt angles $\chi$) of young pulsars can be used to probe some critical issues about the internal physics of neutron stars (NSs), for instance, the number of precession cycles $\xi$ and the internal magnetic field configuration (IMFC) of NSs. Evolution of the dipole magnetic field $B_{\rm d}$ of NSs may play an important role in determining the final results. In this work, a power-law form is adopted to describe the decay of $B_{\rm d}$. In such a scenario, the IMFC and $\xi$ of young pulsars with an ordinary $B_{\rm d}\sim10^{12}-\-10^{13}$ G and a steady braking index $n$ are investigated. Since the tilt angle change rates $\dot{\chi}$ of pulsars with $n<3$ can be theoretically predicted, a test on the power-law decay model can thus be made by comparing the theoretical values to that obtained from observations. However, such a comparison can only be made on the Crab pulsar currently, and the results show that the power-law decay model is inconsistent with the Crab's observations. We suggest that rather than decay, the Crab's $B_{\rm d}$ should increase with time at a rate $\sim12-14$ G/s. A definite conclusion on the validity of the power-law decay model for pulsars with ordinary $B_{\rm d}$ may be given if $\dot{\chi}$ of other pulsars could be measured.

Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case in practice. Federated Semi-Supervised Learning (FSSL) addresses this label deficiency problem, targeting situations where only the server has a small amount of labeled data while clients do not. However, a significant performance gap exists between Centralized Semi-Supervised Learning (SSL) and FSSL. This gap arises from confirmation bias, which is more pronounced in FSSL due to multiple local training epochs and the separation of labeled and unlabeled data. We propose $(FL)^2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization. We show that regularizing the original pseudo-labeling loss is suboptimal, and hence we carefully select unlabeled samples for regularization. We further introduce client-specific adaptive thresholding and learning status-aware aggregation to adjust the training process based on the learning progress of each client. Our experiments on three benchmark datasets demonstrate that our approach significantly improves performance and bridges the gap with SSL, particularly in scenarios with scarce labeled data., Comment: Accepted to NeurIPS 2024

Virtual Network Embedding (VNE) is a challenging combinatorial optimization problem that refers to resource allocation associated with hard and multifaceted constraints in network function virtualization (NFV). Existing works for VNE struggle to handle such complex constraints, leading to compromised system performance and stability. In this paper, we propose a \textbf{CON}straint-\textbf{A}ware \textbf{L}earning framework for VNE, named \textbf{CONAL}, to achieve efficient constraint management. Concretely, we formulate the VNE problem as a constrained Markov decision process with violation tolerance. This modeling approach aims to improve both resource utilization and solution feasibility by precisely evaluating solution quality and the degree of constraint violation. We also propose a reachability-guided optimization with an adaptive reachability budget method that dynamically assigns budget values. This method achieves persistent zero violation to guarantee the feasibility of VNE solutions and more stable policy optimization by handling instances without any feasible solution. Furthermore, we propose a constraint-aware graph representation method to efficiently learn cross-graph relations and constrained path connectivity in VNE. Finally, extensive experimental results demonstrate the superiority of our proposed method over state-of-the-art baselines. Our code is available at https://github.com/GeminiLight/conal-vne.

Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient representation of whole-body motion trajectories within our bilevel model-based predictive control (MPC) framework. We utilize B\'ezier-curve parameterization to represent the optimized collision-free trajectories of two collaborating end-effectors in the first MPC, facilitating fast long-horizon object-oriented motion planning in SE(3) while considering approximated feasibility constraints. This approach is further applied to parameterize whole-body trajectories in the second MPC for whole-body motion generation with predictive admittance control in a relatively short horizon while satisfying whole-body hard constraints. This representation enables two MPCs with continuous properties, thereby avoiding inaccurate model-state transition and dense decision-variable settings in existing MPCs using the discretization method. It strengthens the online execution of the bilevel MPC framework in high-dimensional space and facilitates the generation of consistent commands for our hybrid position/velocity-controlled robot. The simulation comparisons and real-world experiments demonstrate the efficiency and robustness of this approach in various scenarios for static and dynamic obstacle avoidance, and compliant interaction control with the manipulated object and external disturbances., Comment: Under Review for IEEE Transactions on Robotics

Facial parts swapping aims to selectively transfer regions of interest from the source image onto the target image while maintaining the rest of the target image unchanged. Most studies on face swapping designed specifically for full-face swapping, are either unable or significantly limited when it comes to swapping individual facial parts, which hinders fine-grained and customized character designs. However, designing such an approach specifically for facial parts swapping is challenged by a reasonable multiple reference feature fusion, which needs to be both efficient and effective. To overcome this challenge, FuseAnyPart is proposed to facilitate the seamless "fuse-any-part" customization of the face. In FuseAnyPart, facial parts from different people are assembled into a complete face in latent space within the Mask-based Fusion Module. Subsequently, the consolidated feature is dispatched to the Addition-based Injection Module for fusion within the UNet of the diffusion model to create novel characters. Extensive experiments qualitatively and quantitatively validate the superiority and robustness of FuseAnyPart. Source codes are available at https://github.com/Thomas-wyh/FuseAnyPart., Comment: Accepted by the NeurIPS 2024 (Spotlight). Homepage: https://thomas-wyh.github.io/

A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of their training cohorts; their performance drops significantly when confronting unfamiliar agents. To address this shortcoming, we introduce Inverse Attention Agents that adopt concepts from the Theory of Mind, implemented algorithmically using an attention mechanism and trained in an end-to-end manner. Crucial to determining the final actions of these agents, the weights in their attention model explicitly represent attention to different goals. We furthermore propose an inverse attention network that deduces the ToM of agents based on observations and prior actions. The network infers the attentional states of other agents, thereby refining the attention weights to adjust the agent's final action. We conduct experiments in a continuous environment, tackling demanding tasks encompassing cooperation, competition, and a blend of both. They demonstrate that the inverse attention network successfully infers the attention of other agents, and that this information improves agent performance. Additional human experiments show that, compared to baseline agent models, our inverse attention agents exhibit superior cooperation with humans and better emulate human behaviors.

The single variant spin cycloid and associated antiferromagnetic order in multiferroic BiFeO3 can provide a direct and predictable magnetoelectric coupling to ferroelectric order for deterministic switching, and also a key to fundamental understanding of spin transport and magnon-based applications in the system. (111) oriented BiFeO3 supplies an easy magnetic plane for the spin cycloid, but despite previous efforts, achieving deterministic switching of the single spin cycloid over multiple cycles remains challenging due to the presence of multiple spin cycloid domains in the (111) plane. Here we show that anisotropic in-plane strain engineering can stabilize a single antiferromagnetic domain and provide robust, deterministic switching. We grow BiFeO3 on orthorhombic NdGaO3 (011)o [(111)pc] substrates, breaking the spin cycloid degeneracy and by imposing a uniaxial strain in the (111) plane. This stabilization is confirmed through direct imaging with scanning NV microscopy and non-resonant X-ray diffraction. Remarkably, we achieved deterministic and non-volatile 180{\deg} switching of ferroelectric and associated antiferromagnetic domains over 1,000 cycles, significantly outperforming existing approaches. Our findings underscore that anisotropic strain engineering opens up exciting possibilities for (111)pc monodomain BiFeO3 in potential magnetoelectric and emerging magnonic applications., Comment: 17 pages, 4 figures

We report a time-resolved analysis of the accreting X-ray pulsar Cen X-3 using observations carried out by NuSTAR, which cover approximately two binary orbits in different intensity states. The pulse profile is relatively stable over the orbital phase and shows energy dependence. It has an obvious double-peaked shape in the energy band below 15 keV -- with the second pulse peak decreasing as energy increases -- and is gradually dominated by a single peak in higher energy bands. We find that the pulse profile in the energy band of 3-5 keV at high-intensity states shows a subtle triple-peaked shape, with the main peak divided into two subpeaks. We also find a positive correlation between the pulse fraction and both energy and flux. Our spectral analysis reveals that the spectra can be well described by the continuum of Fermi-Dirac cutoff and NPEX models, and the cyclotron line is detected with the centroid energies varying from 26 keV to 29 keV, along with the iron emission line around 6.4 keV. We investigated the dependence between the cyclotron resonant scattering feature (CRSF) centroid energy and luminosity and discuss the theoretical critical luminosity. Although the variation of $E_{\rm cyc}- L_X$ is not distinct, there is a possibility that the critical luminosity lies within the range of $\sim (0.5-4)\times 10^{37}$ erg s$^{-1}$ in the band of $4-78$ keV. The photon index shows a strong positive correlation with luminosity. Our orbital-phase analysis reveals that the spectral parameters show orbital variability, and the highly variable photoelectric absorption may indicate the existence of clumpy stellar winds., Comment: 10 pages, 10 figures

Deep neural networks have been very successful in applications ranging from computer vision and natural language processing to strategy optimization in games. Recently neural networks with sinusoidal activation functions (SinNN) were found to be ideally suited for representing complex natural signals and their fine spatial and temporal details, which makes them effective representations of images, sound, and video, and good solvers of differential equations. However, training SinNN via gradient descent often results in bad local minima, posing a significant challenge when optimizing their weights. Furthermore, when the weights are discretized for better memory and inference efficiency on small devices, we find that a vanishing gradient problem appears on the resulting discrete SinNN (DSinNN). Brute force search provides an alternative way to find the best weights for DSinNN but is intractable for a large number of parameters. We here provide a qualitatively different training method: an algorithm for quantum training of DSinNNs. The quantum training evolves an initially uniform superposition over weight values to one that is guaranteed to peak on the best weights. We demonstrate the algorithm on toy examples and show that it indeed outperforms gradient descent in optimizing the loss function and outperforms brute force search in the time required.

Measurements of $CP$ observables and the CKM angle $\gamma$ are performed in $B^{\pm} \to D K^*(892)^{\pm}$ decays, where $D$ represents a superposition of $D^0$ and $\overline{D}{}^0$ states, using the LHCb dataset collected during Run 1 (2011-2012) and Run 2 (2015-2018). A comprehensive study of this channel is presented with the $D$ meson reconstructed in two-body final states $K^{\pm}\pi^{\mp}$, $K^+K^-$ and $\pi^+\pi^-$; four-body final states $K^{\pm}\pi^{\mp}\pi^{\pm}\pi^{\mp}$ and $\pi^+\pi^-\pi^+\pi^-$; and three-body final states $K^0_{S} \pi^+\pi^-$ and $K^0_{S} K^+ K^-$. This analysis includes the first observation of the suppressed $B^{\pm} \to [\pi^+K^-]_D K^{*\pm}$ and $B^{\pm} \to [\pi^+K^-\pi^+\pi^-]_D K^{*\pm}$ decays. The combined result gives $\gamma=(63\pm 13)^\circ$., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3180/ (LHCb public pages)

Synchrotron maser emission is a leading candidate to explain the coherent emission from Fast Radio Bursts (FRBs). This mechanism requires a population inversion in order to operate. We show that nonresonant interactions between Alfv\'{e}n waves and a relativistic plasma result in the formation of population inversions across a wide range of magnetizations, $\sigma\gtrsim10^{-4}$, and temperatures, $10^{-2} \leq k_bT/mc^2 \leq 3$, spanning the parameters expected in FRB environments. We calculate the fraction of energy contained in the inversion across the whole of this parameter space for the first time and we show that energy fractions of $f_{inv}\sim 0.3$ are achieved for high magnetizations $\sigma >1$. The population inversion forms on time-scales compatible with the typical dynamical time-scales of magnetars for all magnetizations. Furthermore, we provide physical explanations for the behaviour of the interaction in different magnetization regimes, and identify the important characteristic values at which this behaviour changes. We also show that the mechanism is capable of producing an FRB signal at GHz frequencies in a relativistic magnetar wind close to the light cylinder and that this signal can escape the magnetar environment without significant damping., Comment: 19 pages, 21 figures, submitted to MNRAS

The James Webb Space Telescope (JWST) has uncovered an abundant population of compact, extremely red, and X-ray weak objects at $z\gtrsim4$, knows as ``Little Red Dots" (LRDs). These objects exhibit spectral energy distributions that resemble both active galactic nuclei (AGN) and stellar population templates. However, whether dominated by AGN activity or compact star formation, the high redshifts and masses/luminosities of LRDs, coupled with their significant abundance, present potential challenges to the standard $\Lambda$CDM model. In this work, we proposes a novel cosmic interpretation of this anomaly, suggesting that these LRDs are likely massive galaxies seeded by supermassive primordial black holes (SMPBHs) came into being in the very early universe. We analyze 434 known LRDs from the 0.54 ${\rm deg}^2$ COSMOS-Web survey and test the hypothesis that they originated from SMPBHs assuming sub-Eddington accretion. According to our result, SMPBHs actually could lead to the existence of more LRDs, even at higher redshifts ($z>8$)., Comment: 7 pages, 4 figures

Self-interacting dark matter (SIDM) can address the small-scale anomalies and previous researches focused on such a SIDM heavier than GeV, for which the self-scattering cross-section is in the quantum resonance region and has a non-trivial velocity dependence. For a SIDM lighter than GeV, the self-scattering cross-section falls within the Born region. In this work, considering the constraints from CMB, BBN and the DM relic density, we investigate the direct detection of the Higgs portal for a sub-GeV SIDM with a scalar mediator. For this end, we consider two approaches : one is the cosmic-ray accelerated dark matter (CRDM) scattering off the nucleon, the other is the electron recoil caused by the halo dark matter. We present direct detection limits for the parameter space of light SIDM and scalar mediator. We find that the detectability in either approach needs a sizable mediator-Higgs mixing angle ($\sin\theta$) which is larger than one for the CRDM approach and larger than $10^{-3}$ for the electron recoil approach. While the former case cannot be realized in the Higgs-portal light SIDM model with a scalar mediator, the latter case may also be constrained by some astrophysical observations or beam dump experiment. Anyway, even if other constraints are quite stringent, the direct detection may provide independent limits for such a sub-GeV SIDM., Comment: 22 pages, 7 figures

Text-to-video models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant challenge. To generate high-quality, dynamic, and temporally consistent long videos, this paper presents ARLON, a novel framework that boosts diffusion Transformers with autoregressive models for long video generation, by integrating the coarse spatial and long-range temporal information provided by the AR model to guide the DiT model. Specifically, ARLON incorporates several key innovations: 1) A latent Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input latent space of the DiT model into compact visual tokens, bridging the AR and DiT models and balancing the learning complexity and information density; 2) An adaptive norm-based semantic injection module integrates the coarse discrete visual units from the AR model into the DiT model, ensuring effective guidance during video generation; 3) To enhance the tolerance capability of noise introduced from the AR inference, the DiT model is trained with coarser visual latent tokens incorporated with an uncertainty sampling module. Experimental results demonstrate that ARLON significantly outperforms the baseline OpenSora-V1.2 on eight out of eleven metrics selected from VBench, with notable improvements in dynamic degree and aesthetic quality, while delivering competitive results on the remaining three and simultaneously accelerating the generation process. In addition, ARLON achieves state-of-the-art performance in long video generation. Detailed analyses of the improvements in inference efficiency are presented, alongside a practical application that demonstrates the generation of long videos using progressive text prompts. See demos of ARLON at \url{http://aka.ms/arlon}.

The paper introduces a novel tangential-normal ($t$-$n$) decomposition for finite element differential forms. Its main contribution is the development of a $t$-$n$ basis where the degrees of freedom and shape functions are explicitly dual to each other. This duality simplifies the assembly of stiffness matrices and enhances the efficiency of interpolation and numerical integration in finite element methods. Additionally, the well-documented Lagrange element basis can be used to expedite implementation. This paper focuses on the full polynomial spaces, excluding trimmed polynomial differential forms, and provides a new perspective on constructing finite element differential forms., Comment: 21 pages, 3 figures

The dynamic mechanisms underlying single charge-exchange reactions have been investigated using a theoretical framework that combines the Isospin-dependent Quantum Molecular Dynamics (IQMD) model with the statistical decay model GEMINI++. Two distinct channels contribute to the single isobaric charge-exchange reaction: quasi-elastic channel, where neutron-proton scattering drives the charge-exchange, and inelastic channel, where the $\Delta$ particle is produced during the process. In a referenced study [Phys.RevC 106.014618(2022)], experimental data have revealed that the inelastic channel accounts for approximately 50 percent of the single isobaric charge-exchange reaction. However, our current model fails in reproducing the significant contribution of inelastic channel unless the novel medium recoil mode associated with $\Delta$ production is considered in the calculations. Notably, this in-medium effect arising from inelastic nucleon-nucleon collisions is not yet incorporated into mainstream microscopic transport models. The dynamical properties of protons and pions emitting in the single isobaric charge-exchange reactions are predicted. This exploration of in-medium effects adds a valuable dimension to our understanding of the intricate dynamics involved in single charge-exchange reactions.

During the dielectric breakdown process of thin solid-state nanopores, the application of high voltages may cause the formation of multi-nanopores on one chip, which number and sizes are important for their applications. Here, simulations were conducted to mimic the investigation of in situ nanopore detection with scanning ion conductance microscopy (SICM). Results show that SICM can provide accurate nanopore location and relative pore size. Detection resolution is influenced by the dimensions of the applied probe and separation between the probe and membranes, which can be enhanced under large voltages or a concentration gradient., Comment: 22 pages, 6 figuers

Bipolar nanopores, with asymmetric charge distributions, can induce significant ionic current rectification (ICR) at ultra-short lengths, finding potential applications in nanofluidic devices, energy conversion, and other related fields. Here, with simulations, we investigated the characteristics of ion transport and modulation of ICR inside bipolar nanopores. With bipolar nanopores of half-positive and half-negative surfaces, the most significant ICR phenomenon appears at various concentrations. In these cases, ICR ratios are independent of electrolyte types. In other cases where nanopores have oppositely charged surfaces in different lengths, ICR ratios are related to the mobility of anions and cations. The pore length and surface charge density can enhance ICR. As the pore length increases, ICR ratios first increase and then approach their saturation which is determined by the surface charge density. External surface charges of nanopores can promote the ICR phenomenon mainly due to the enhancement of ion enrichment inside nanopores by external surface conductance. The effective width of exterior charged surfaces under various conditions is also explored, which is inversely proportional to the pore length and salt concentration, and linearly related to the pore diameter, surface charge density, and applied voltage. Our results may provide guidance for the design of bipolar porous membranes., Comment: 31 pages, 7 figures

Multimodal Large Language Models (MLLMs) have gained significant attention due to their impressive capabilities in multimodal understanding. However, existing methods rely heavily on extensive modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. In this paper, we propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities that enables MLLMs to continually EVolve on modalities for $\mathbb{X}$-modal reasoning. We leverage the concept of Continual Learning and develop an incremental training strategy atop pre-trained MLLMs, enabling their expansion to new modalities using uni-modal data, without executing joint-modal pretraining. In detail, a novel Adapter-in-Adapter (AnA) framework is introduced, in which uni-modal and cross-modal adapters are seamlessly integrated to facilitate efficient modality alignment and collaboration. Additionally, an MoE-based gating module is applied between two types of adapters to further enhance the multimodal interaction. To investigate the proposed method, we establish a challenging benchmark called Continual Learning of Modality (MCL), which consists of high-quality QA data from five distinct modalities: image, video, audio, depth and point cloud. Extensive experiments demonstrate the effectiveness of the proposed AnA framework on learning plasticity and memory stability during continual learning. Furthermore, PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%. Our code locates at https://github.com/JiazuoYu/PathWeave

Retrieval-Augmented Generation (RAG) has been an effective approach to mitigate hallucinations in large language models (LLMs) by incorporating up-to-date and domain-specific knowledge. Recently, there has been a trend of storing up-to-date or copyrighted data in RAG knowledge databases instead of using it for LLM training. This practice has raised concerns about Membership Inference Attacks (MIAs), which aim to detect if a specific target document is stored in the RAG system's knowledge database so as to protect the rights of data producers. While research has focused on enhancing the trustworthiness of RAG systems, existing MIAs for RAG systems remain largely insufficient. Previous work either relies solely on the RAG system's judgment or is easily influenced by other documents or the LLM's internal knowledge, which is unreliable and lacks explainability. To address these limitations, we propose a Mask-Based Membership Inference Attacks (MBA) framework. Our framework first employs a masking algorithm that effectively masks a certain number of words in the target document. The masked text is then used to prompt the RAG system, and the RAG system is required to predict the mask values. If the target document appears in the knowledge database, the masked text will retrieve the complete target document as context, allowing for accurate mask prediction. Finally, we adopt a simple yet effective threshold-based method to infer the membership of target document by analyzing the accuracy of mask prediction. Our mask-based approach is more document-specific, making the RAG system's generation less susceptible to distractions from other documents or the LLM's internal knowledge. Extensive experiments demonstrate the effectiveness of our approach compared to existing baseline models.

In this work, we first present an adaptive deterministic block coordinate descent method with momentum (mADBCD) to solve the linear least-squares problem, which is based on Polyak's heavy ball method and a new column selection criterion for a set of block-controlled indices defined by the Euclidean norm of the residual vector of the normal equation. The mADBCD method eliminates the need for pre-partitioning the column indexes of the coefficient matrix, and it also obviates the need to compute the Moore-Penrose pseudoinverse of a column sub-matrix at each iteration. Moreover, we demonstrate the adaptability and flexibility in the automatic selection and updating of the block control index set. When the coefficient matrix has full rank, the theoretical analysis of the mADBCD method indicates that it linearly converges towards the unique solution of the linear least-squares problem. Furthermore, by effectively integrating count sketch technology with the mADBCD method, we also propose a novel count sketch adaptive block coordinate descent method with momentum (CS-mADBCD) for solving highly overdetermined linear least-squares problems and analysis its convergence. Finally, numerical experiments illustrate the advantages of the proposed two methods in terms of both CPU times and iteration counts compared to recent block coordinate descent methods.

Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data. Integrating ensemble-based DA methods with nonlinear deep learning models is computationally expensive and may suffer from large sampling errors. To address these challenges, we introduce a deep learning framework designed to simultaneously provide accurate forecasts and efficient DA. It is named Conditional Gaussian Koopman Network (CGKN), which transforms general nonlinear systems into nonlinear neural differential equations with conditional Gaussian structures. CGKN aims to retain essential nonlinear components while applying systematic and minimal simplifications to facilitate the development of analytic formulae for nonlinear DA. This allows for seamless integration of DA performance into the deep learning training process, eliminating the need for empirical tuning as required in ensemble methods. CGKN compensates for structural simplifications by lifting the dimension of the system, which is motivated by Koopman theory. Nevertheless, CGKN exploits special nonlinear dynamics within the lifted space. This enables the model to capture extreme events and strong non-Gaussian features in joint and marginal distributions with appropriate uncertainty quantification. We demonstrate the effectiveness of CGKN for both prediction and DA on three strongly nonlinear and non-Gaussian turbulent systems: the projected stochastic Burgers--Sivashinsky equation, the Lorenz 96 system, and the El Ni\~no-Southern Oscillation. The results justify the robustness and computational efficiency of CGKN.