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Ultra-long-range Rydberg molecules (ULRMs) have attracted significant interest due to their unique electronic properties and potential applications in quantum technologies. We theoretically investigate the formation and characteristics of heteronuclear ULRMs, focusing on Rb-Cs systems. We explore the vibrational energy levels of heteronuclear nD ULRMs and compare them with homonuclear counterparts. We also predict the formation of polyatomic heteronuclear ULRMs, discussing how the binding energy and spectral features evolve as the number of ground-state atoms increases. Our theoretical predictions are presented in terms of molecular spectra and provide insight into the formation dynamics of these systems. The study further explores the potential applications of heteronuclear ULRMs in quantum information processing, quantum simulation, and precision measurements, offering new avenues for future research in many-body physics and quantum technologies.

We investigate the molecular environment of the supernova remnant (SNR) Kesteven 67 (G18.8+0.3) using observations in $^{12}$CO, $^{13}$CO, HCO$^+$,and HCN lines and possible associated $\gamma$-ray emission using 16-yr Fermi-LAT observation. We find that the SNR is closely surrounded by a molecular belt in the southeastern boundary, with the both recessed in the band-like molecular gas structure along the Galactic plane. The asymmetric molecular line profiles are widely present in the surrounding gas around local-standard-of-rest velocity +20 km s$^{-1}$. The secondary components centered at $\sim$+16km s$^{-1}$ in the belt and $\sim$+26 km s$^{-1}$ in the northern clump can be ascribed to the motion of a wind-blown molecular shell. This explanation is supported by the position-velocity diagram along a line cutting across the remnant, which shows an arc-like pattern, suggesting an expanding gas structure. With the simulation of chemical effects of shock propagation, the abundance ratios $N$(HCO$^+$)/$N$($^{12}$CO) $\sim2.6\times 10^{-5}$--$3.6\times 10^{-4}$ obtained in the belt can be more naturally interpreted by the wind-driven bubble shock than by the SNR shock. The belt and northern clump are very likely to be parts of an incomplete molecular shell of bubble driven by O-type progenitor star's wind. The analysis of 0.2--500 GeV $\gamma$-ray emission uncovers a possible point source (`Source~A') about 6.5$\sigma$ located in the north of the SNR, which essentially corresponds to northern molecular clump. Our spectral fit of the emission indicates that a hadronic origin is favored by the measured Galactic number ratio between CR electrons and protons $\sim0.01$., Comment: 14 pages, 10 figures, accepted by MNRAS

Mobility edges (MEs) are critical boundaries in disordered quantum systems that separate localized from extended states, significantly affecting transport properties and phase transitions. Although MEs are well-understood in single-photon systems, their manifestation in many-body contexts remains an active area of research. In this work, we investigate a one-dimensional Bose-Hubbard chain with a quasiperiodic potential modulating photon-photon interactions, effectively creating a mosaic lattice. We identify MEs for doublon states (i.e, bound photon pairs resulting from strong interactions) within the two-photon subspace. Our analytical solutions and numerical simulations confirm the existence of these MEs, extending single-photon MEs theories to the two-photon regime. Additionally, we analyze the dynamics of two emitters coupled to the waveguide, enabling the emission of supercorrelated photon pairs into the waveguide. Our findings reveal that coupling to extended states results in Markovian dynamics, characterized by exponentially supercorrelated decay, while coupling to localized states gives rise to non-Markovian dynamics, marked by suppressed decay and persistent oscillations. Here, a transition from Markovian to non-Markovian behavior occurs around the MEs of the doublons. Finally, we propose a feasible experimental implementation using superconducting circuits, providing a platform to observe the interplay between interactions and disorder in quantum systems., Comment: 14 pages; 11 figures

Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertain if the image generation quality is consistent across different demographic subgroups. To address this critical concern, we present the first comprehensive study on the fairness of medical text-to-image diffusion models. Our extensive evaluations of the popular Stable Diffusion model reveal significant disparities across gender, race, and ethnicity. To mitigate these biases, we introduce FairDiffusion, an equity-aware latent diffusion model that enhances fairness in both image generation quality as well as the semantic correlation of clinical features. In addition, we also design and curate FairGenMed, the first dataset for studying the fairness of medical generative models. Complementing this effort, we further evaluate FairDiffusion on two widely-used external medical datasets: HAM10000 (dermatoscopic images) and CheXpert (chest X-rays) to demonstrate FairDiffusion's effectiveness in addressing fairness concerns across diverse medical imaging modalities. Together, FairDiffusion and FairGenMed significantly advance research in fair generative learning, promoting equitable benefits of generative AI in healthcare., Comment: The data and code are made publicly available at https://github.com/Harvard-Ophthalmology-AI-Lab/FairDiffusion

The dynamics of superfluid systems exhibit significant similarities to their classical counterparts, particularly in the phenomenon of vortex shedding triggered by a moving obstacle. In such systems, the universal behavior of shedding patterns can be classified using the classical concept of the Reynolds number $Re=\frac{v \sigma}{\nu}$ (characteristic length scale $\sigma$, velocity $v$ and viscosity $\nu$), which has been shown to generalize to quantum systems at absolute zero temperature. However, it remains unclear whether this universal behavior holds at finite temperatures, where viscosity arises from two distinct sources: thermal excitations and quantum vortex viscosity. Using a holographic model of finite-temperature superfluids, we investigate the vortex shedding patterns and identify two distinct regimes without quantum counterparts: a periodic vortex dipole pattern and a vortex dipole train pattern. By calculating the shedding frequency, Reynolds number, and Strouhal number, we find that these behaviors are qualitatively similar to empirical observations in both classical and quantum counterparts, which imply the robustness of vortex shedding dynamics at finite-temperature superfluid systems., Comment: 22 pages, 8 figures

Recent advancements in astrophysical observations of compact stars, particularly the new and updated NICER constraints, have provided mass-radius ($M$-$R$) data for pulsars spanning masses from 1 to $2\,M_{\odot}$. These data offer a unique opportunity to test modern theories of dense matter using multi-messenger constraints. Covariant density functional (CDF) models of nuclear matter, which capture a broad range of nuclear and astrophysical phenomena, provide a robust theoretical framework to interpret these observations. This study applies the Bayesian framework to a class of CDF models with density-dependent meson-nucleon couplings, specifically those based on nucleonic degrees of freedom. By incorporating the latest multi-messenger constraints, we impose tighter limits on the parameter space of these models and assess their consistency with observational data. Our analysis advances previous efforts by refining the density-dependence parameterization and integrating recent $M$-$R$ ellipses. This enables more stringent evaluations of dense matter models in light of new astrophysical observations., Comment: 11 pages, 4 figures and 4 tables

Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated content itself, neglecting its effectiveness in real-world applications. To bridge this gap, we propose AIGI-VC, a quality assessment database for AI-Generated Images in Visual Communication, which studies the communicability of AIGIs in the advertising field from the perspectives of information clarity and emotional interaction. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning. We conduct an empirical study of existing representative IQA methods and large multi-modal models on the AIGI-VC dataset, uncovering their strengths and weaknesses., Comment: AAAI-2025; Project page: https://github.com/ytian73/AIGI-VC

We describe a three-dimensional (3D) magneto-optical trap (MOT) capable of simultaneously capturing 85Rb and 133Cs atoms. Unlike conventional setups, our system utilizes two separate laser systems that are combined before entering the vacuum chamber, enabling the simultaneous trapping of two different atomic species. Additionally, in our 3D MOT configuration, two (of three) pairs of laser beams are not orthogonal to the chamber surfaces but are aligned at a 45{\deg} angle. With a total trapping laser power of 8 mW and repump laser power of 4 mW for Rb atoms, and a total trapping laser power of 7.5 mW and repump laser power of 1.5 mW for Cs atoms, we achieve optical depths (OD) of 3.71 for Rb and 3.45 for Cs, demonstrating efficient trapping for both species. Our 3D MOT setup allows full horizontal optical access to the trapped atomic ensembles without spatial interference from the trapping or repump laser beams. Moreover, the red detuning for trapping both atomic species is smaller than in traditional configurations. This system offers a versatile platform for exploring complex phenomena in ultracold atom physics, such as Rydberg molecule formation and interspecies interactions., Comment: 7 pages, 5 figures

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline's capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills.

Shock waves from supernova remnants (SNRs) have strong influence on the physical and chemical properties of molecular clouds (MCs). Shocks propagating into magnetized MCs can be classified into "jump" J-shock and "continuous" C-shock. The molecular chemistry in the re-formed molecular gas behind J-shock is still not well understood, which will provide a comprehensive view of the chemical feedback of SNRs and the chemical effects of J-shock. We conducted a W-band (71.4-89.7 GHz) observation toward a re-formed molecular clump behind a J-shock induced by SNR W51C with the Yebes 40 m radio telescope to study the molecular chemistry in the re-formed molecular gas. Based on the local thermodynamic equilibrium (LTE) assumption, we estimate the column densities of HCO+, HCN, C2H and o-c-C3H2, and derive the maps of their abundance ratios with CO. The gas density is constrained by non-LTE analysis of the HCO+ J=1-0 line. We obtain the following abundance ratios: $N({\rm HCO^+})/N({\rm CO})\sim (1.0\text{--}4.0)\times 10^{-4}$, $N({\rm HCN})/N({\rm CO})\sim (1.8\text{--}5.3)\times 10^{-4}$, $N({\rm C_2H})/N({\rm CO})\sim (1.6\text{--}5.0)\times 10^{-3}$, and $N({o\text{-}c\text{-}{\rm C_3H_2}})/N({\rm CO})\sim (1.2\text{--}7.9)\times 10^{-4}$. The non-LTE analysis suggests that the gas density is $n_{\rm H_2}\gtrsim 10^4\rm \ cm^{-3}$. We find that the N(C2H)/N(CO) and N(o-c-C3H2)/N(CO) are higher than typical values in quiescent MCs and shocked MCs by 1-2 orders of magnitude, which can be qualitatively attributed to the abundant C+ and C at the earliest phase of molecular gas re-formation. The Paris-Durham shock code can reproduce, although not perfectly, the observed abundance ratios, especially the enhanced N(C2H)/N(CO) and N(o-c-C3H2)/N(CO), with J-shocks propagating in to both non-irradiated and irradiated molecular gas with a preshock density of $n_{\rm H}=2\times 10^3\rm \ cm^{-3}$., Comment: 11 pages, 9 figure, accepted for publication in A&A, abstract adjusted for the requirements of Arxiv

Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to signal processing of such topological signals. Most of the previous topological signal processing algorithms treat node and edge signals separately and work under the hypothesis that the true signal is smooth and/or well approximated by a harmonic eigenvector of the Hodge-Laplacian, which may be violated in practice. Here we propose Dirac-equation signal processing, a framework for efficiently reconstructing true signals on nodes and edges, also if they are not smooth or harmonic, by processing them jointly. The proposed physics-inspired algorithm is based on the spectral properties of the topological Dirac operator. It leverages the mathematical structure of the topological Dirac equation to boost the performance of the signal processing algorithm. We discuss how the relativistic dispersion relation obeyed by the topological Dirac equation can be used to assess the quality of the signal reconstruction. Finally, we demonstrate the improved performance of the algorithm with respect to previous algorithms. Specifically, we show that Dirac-equation signal processing can also be used efficiently if the true signal is a non-trivial linear combination of more than one eigenstate of the Dirac equation, as it generally occurs for real signals., Comment: (14 pages, 7 figures)

Recently Hollands, Wald and Zhang proposed a new formula for the entropy of a dynamical black hole for an arbitrary theory of gravity obtained from a diffeomorphism covariant Lagrangian via the Noether charge method. We present an alternative, pedagogical derivation of the dynamical black hole entropy for $f(R)$ gravity as well as canonical scalar-tensor theory by means of conformal transformations. First, in general relativity we generalize Visser and Yan's pedagogical proof of the non-stationary physical process first law to black holes that may not be in vacuum, and give a pedagogical derivation of the second-order behavior of the dynamical black hole entropy for vacuum perturbations by considering the second-order variation of the Raychaudhuri equation. Second, we apply the derivation for general relativity to theories in the Einstein frames, and then recast the conclusions in their original frames. We show that the dynamical black hole entropy formulae of these theories satisfy both the non-stationary physical process first law and the non-stationary comparison first law via the Einstein frame. We further study the second-order behavior of the dynamical black hole entropy for vacuum perturbations, and observe that the second law is obeyed at second order in those theories. Using the Einstein frame, we also determine the relationship between the dynamical black hole entropy and the Wald entropy of the generalized apparent horizon in the original frame.

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted camera observations remains a critical challenge. In this paper, we introduce a video-based spatial perception framework that leverages 3D spatial representations to address environmental variability, with a focus on handling lighting changes. Our approach integrates a novel image augmentation technique, AugBlender, with a state-of-the-art monocular depth estimation model trained on internet-scale data. Together, these components form a cohesive system designed to enhance robustness and adaptability in dynamic scenarios. Our results demonstrate that our approach significantly boosts the success rate across diverse camera exposures, where previous models experience performance collapse. Our findings highlight the potential of video-based spatial perception models in advancing robustness for end-to-end robotic learning, paving the way for scalable, low-cost solutions in embodied intelligence., Comment: 8 pages, 5 figures

Immersion minor is an important variant of graph minor, defined through an injective mapping from vertices in a smaller graph $H$ to vertices in a larger graph $G$ where adjacent elements of the former are connected in the latter by edge-disjoint paths. Here, we consider the immersion problem in the emerging field of hypergraphs. We first define hypergraph immersion by extending the injective mapping to hypergraphs. We then prove that finding a hypergraph immersion is fixed-parameter tractable, namely, there exists an $O(N^6)$ polynomial-time algorithm to determine whether a fixed hypergraph $H$ can be immersed in a hypergraph $G$ with $N$ vertices. Additionally, we present the dual hypergraph immersion problem and provide further characteristics of the algorithmic complexity.

We construct a soliton train configuration with holographic superfluid model under AdS soliton background. We investigate the stability of a soliton train using Bloch waves under two distinct quantization schemes. Upon imposing a minor perturbation on the soliton train system, it has been observed that there exist two elastic modes and one phonon mode. Most importantly, we find, under soliton train background, that there is a rich phase diagram concerning the chemical potential under standard quantization. Nevertheless, there are no unstable modes under alternative quantization., Comment: 11 pages,13 figures. references added, to appear in PRD

Deep learning has achieved remarkable success in image classification and segmentation tasks. However, fairness concerns persist, as models often exhibit biases that disproportionately affect demographic groups defined by sensitive attributes such as race, gender, or age. Existing bias-mitigation techniques, including Subgroup Re-balancing, Adversarial Training, and Domain Generalization, aim to balance accuracy across demographic groups, but often fail to simultaneously improve overall accuracy, group-specific accuracy, and fairness due to conflicts among these interdependent objectives. We propose the Fair Distillation (FairDi) method, a novel fairness approach that decomposes these objectives by leveraging biased ``teacher'' models, each optimized for a specific demographic group. These teacher models then guide the training of a unified ``student'' model, which distills their knowledge to maximize overall and group-specific accuracies, while minimizing inter-group disparities. Experiments on medical imaging datasets show that FairDi achieves significant gains in both overall and group-specific accuracy, along with improved fairness, compared to existing methods. FairDi is adaptable to various medical tasks, such as classification and segmentation, and provides an effective solution for equitable model performance.

We study 1+1 dimensional relativistic non-resistive magnetohydrodynamics (MHD) with longitudinal boost invariance and shear stress tensor. Several analytical solutions that describe the fluid temperature evolution under the equation of state (EoS) $\varepsilon=3p$ are derived, relevant to relativistic heavy-ion collisions. Extending the Victor-Bjorken ideal MHD flow to include non-zero shear viscosity, two perturbative analytical solutions for the first-order (Navier-Stokes) approximation are obtained. For small, power-law evolving external magnetic fields, our solutions are stable and show that both magnetic field and shear viscosity cause fluid heating with an early temperature peak, align with the numerical results. In the second-order (Israel-Stewart) theory, our findings show that the combined presence of magnetic field and shear viscosity leads to a slow cooling rate of fluid temperature, with initial shear stress significantly affecting temperature evolution of QGP., Comment: 12 pages, 7 figures. Comments are welcome

Broken symmetry plays a pivotal role in determining the macroscopic electrical, optical, magnetic, and topological properties of materials. Circular dichroism (CD) has been widely employed to probe broken symmetry in various systems, from small molecules to bulk crystals, but designing CD responses on demand remains a challenge, especially for antiferromagnetic materials. Here, we develop a cavity-enhanced CD technique to sensitively probe the magnetic order and broken symmetry in the van der Waals antiferromagnet FePS3. By introducing interfacial inversion asymmetry in cavity-coupled FePS3 crystals, we demonstrate that the induced CD is strongly coupled with the zig-zag antiferromagnetic order of FePS3 and can be tuned both spectrally and in magnitude by varying the cavity length and FePS3 thickness. Our findings open new avenues for using cavity-modulated CD as a sensitive diagnostic probe to detect weak broken symmetries, particularly at hidden interfaces, and in systems exhibiting hidden spin polarization or strong correlations.

Supernova remnants (SNRs) can exert strong influence on molecular clouds (MCs) through interaction by shock wave and cosmic rays. In this paper, we present our mapping observation of HCO+ and HCN 1-0 lines towards 13 SNRs interacting with MCs, together with archival data of CO isotopes. Strong HCO+ emission is found in the fields of view (FOVs) of SNRs W30, G9.7-0.0, Kes 69, 3C 391, 3C 396, W51C, HC 40, and CTB109 in the local-standard-of-rest (LSR) velocity intervals in which they are suggested to show evidence of SNR-MC interaction. We find an incomplete 12CO shell surrounding G9.7-0.0 with an expanding motion. This shell may be driven by the stellar wind of the SNR progenitor. We also find an arc of 12CO gas spatially coincident with the northwestern radio shell of Kes 69. As for the HCO+ line emission, SNRs 3C 391 and W51C exhibit significant line profile broadening indicative of shock perturbation, and CTB109 exhibits a possible blue-shifted line wing brought by shock interaction. We do not find significant variation of the I(HCO+)/I(HCN) line ratio between broad-line and narrow-line regions, among different SNRs, and between MCs associated with SNRs and typical Galactic MCs. Therefore, we caution on using the I(HCO+)/I(HCN) line ratio as a diagnostic of SNR feedback and CR ionization. We also estimate the N(HCO+)/N(CO) abundance ratio in 11 regions towards the observed SNRs, but they show little difference from the typical values in quiescent MCs, possibly because N(HCO+)/N(CO) is not an effective tracer of CR ionization., Comment: 19 pages, 8 figures, accepted for publication in ApJ

In space-based gravitational wave observatories such as Taiji, LISA, and TianQin, data gaps are inevitable due to mission design, implementation, and the long duration of observations. These data gaps degrade data quality and cause spectral leakage during Fourier transformations. Since ringdown signals are a key scientific objective for these observatories, it is crucial to assess the impact of data gaps on ringdown signal observations. This study employs LISA's science requirement of maintaining a duty cycle of at least 75% to evaluate the worst-case impact of data gaps, and uses massive black hole binary catalogs to assess the average effects. Our findings indicate that, on average, data gaps increase parameter estimation errors by approximately 2.1 times for the (2,2) mode and by about 1.6 times for the (3,3) mode. Joint observation is commonly employed to alleviate the impact of data gaps. Similarly, we have evaluated the effects of joint observation with two configurations, Taiji-LISA and Taiji-TianQin, which demonstrate notable mitigation of the effects of data gaps. This work provides a quantitative assessment of data gaps on ringdown signals and highlights the significance of joint observation., Comment: 14 pages, 7 figures

Based on the hyperboloidal framework, we research the dynamical process of charged de Sitter black holes scattered by a charged scalar field. From the linear perturbation analysis, with the coupling strength within a critical interval, the charged scalar field with a superradiance frequency can induce the instability of the system. To reveal the real-time dynamics of such an instability, the nonlinear numerical simulation is implemented. The results show that the scalar field grows exponentially in the early stages and drastically extracts the charge from the black hole due to the superradiance, analogous to the charged black hole in a closed system. Differently, after saturation, the scalar field can not coexist with the central black hole stably and dissipates beyond the cosmological horizon slowly, leaving behind a bald black hole., Comment: 16 pages, 12 figures

In this paper, we extend the study of holographic superfluids from planar topology to spherical topology, inspired by recent studies on Bose-Einstein condensation (BEC) superfluid on shell-shaped geometry. We mainly focus on superfluid phase transition and Quasi Normal Modes (QNMs). It turns out that the critical temperature for the superfluid phase transition on the sphere is higher than that in the planar case. We investigated four different solutions in the backgrounds of large black hole and small black hole. The calculation of free energy selects the most stable solution. Finally, after calculating the quasi-normal modes and their dynamic behavior, we obtained three different channels similar to the planar superfluid case, along with the "first" hydrodynamic excitation, which may be associated with Berezinskii-Kosterlitz-Thoules (BKT) transition., Comment: 18 pages, 14 figures

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena, offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. Utilizing a high-intensity muon beam, a Michel electron magnetic spectrometer and a positron transport solenoid together with a positron detection system, MACE aims to discover or constrain this rare process at the conversion probability beyond the level of $10^{-13}$. This report provides an overview of the theoretical framework and detailed experimental design in the search for the muonium-to-antimuonium conversion., Comment: 115 pages, 59 figures

The semantic similarity between sample expressions measures the distance between their latent 'meaning'. Such meanings are themselves typically represented by textual expressions, often insufficient to differentiate concepts at fine granularity. We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the imagery they evoke. While this is not possible with humans, generative models allow us to easily visualize and compare generated images, or their distribution, evoked by a textual prompt. Therefore, we characterize the semantic similarity between two textual expressions simply as the distance between image distributions they induce, or 'conjure.' We show that by choosing the Jensen-Shannon divergence between the reverse-time diffusion stochastic differential equations (SDEs) induced by each textual expression, this can be directly computed via Monte-Carlo sampling. Our method contributes a novel perspective on semantic similarity that not only aligns with human-annotated scores, but also opens up new avenues for the evaluation of text-conditioned generative models while offering better interpretability of their learnt representations.

The possible existence of an exotic phase of matter rigid like a solid but able to sustain persistent and dissipation-less flow like a superfluid, a "supersolid", has been the subject of intense theoretical and experimental efforts since the discovery of superfluidity in Helium-4. Recently, it has been proposed that nonlinear periodic modulations known as cnoidal waves, that naturally emerge in Bose-Einstein condensates, provide a promising platform to find and study supersolidity in nonequilibrium phases of matter. Nevertheless, so far the analysis has been limited to a one-dimensional zero-temperature system. By combining the dissipative Gross-Pitaevskii equation with a finite temperature holographic model, we show that the proposed cnoidal wave supersolid phases of matter are always energetically and dynamically unstable at finite temperature. We ascribe this instability to the dynamics of the "elastic" Goldstone mode arising as a direct consequence of translational order. Finally, we study the dynamical instability of the supersolid state and connect it to the production of topological defects during the relaxation towards a final homogeneous equilibrium state with smaller superflow current, similar to the Landau instability dynamics in superfluids. Our results suggest that the supersolidity of cnoidal waves can emerge only as a transient phenomenon and not as a truly equilibrium and stable ground state., Comment: 12 pages, 10 figures

Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a "virtual contrast agent" to synthesize X-ray angiographies via disentanglement representation learning and vessel semantic guidance, thereby reducing the reliance on iodinated contrast agents during interventional procedures. Specifically, our approach disentangles X-ray angiographies into background and vessel components, leveraging medical prior knowledge. A specialized predictor then learns to map the interrelationships between these components. Additionally, a vessel semantic-guided generator and a corresponding loss function are introduced to enhance the visual fidelity of generated images. Experimental results on the XCAD dataset demonstrate the state-of-the-art performance of our CAS-GAN, achieving a FID of 5.87 and a MMD of 0.016. These promising results highlight CAS-GAN's potential for clinical applications., Comment: IEEE Symposium Series on Computational Intelligence (SSCI 2025)

Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malicious instruction prompts. While current methods effectively address jailbreak risks, they share common limitations: 1) Judging harmful responses from the prefill-level lacks utilization of the model's decoding outputs, leading to relatively lower effectiveness and robustness. 2) Rejecting potentially harmful responses based on a single evaluation can significantly impair the model's helpfulness.This paper examines the LLMs' capability to recognize harmful outputs, revealing and quantifying their proficiency in assessing the danger of previous tokens. Motivated by pilot experiment results, we design a robust defense mechanism at the decoding level. Our novel decoder-oriented, step-by-step defense architecture corrects harmful queries directly rather than rejecting them outright. We introduce speculative decoding to enhance usability and facilitate deployment to boost secure decoding speed. Extensive experiments demonstrate that our approach improves model security without compromising reasoning speed. Notably, our method leverages the model's ability to discern hazardous information, maintaining its helpfulness compared to existing methods., Comment: 19 pages, 9 figures

Hallucinations in large vision-language models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input, which impairs their reliability. Recent studies often attribute hallucinations to a lack of understanding of visual input, yet ignore a more fundamental issue: the model's inability to effectively extract or decouple visual features. In this paper, we revisit the hallucinations in LVLMs from an architectural perspective, investigating whether the primary cause lies in the visual encoder (feature extraction) or the modal alignment module (feature decoupling). Motivated by our findings on the preliminary investigation, we propose a novel tuning strategy, PATCH, to mitigate hallucinations in LVLMs. This plug-and-play method can be integrated into various LVLMs, utilizing adaptive virtual tokens to extract object features from bounding boxes, thereby addressing hallucinations caused by insufficient decoupling of visual features. PATCH achieves state-of-the-art performance on multiple multi-modal hallucination datasets. We hope this approach provides researchers with deeper insights into the underlying causes of hallucinations in LVLMs, fostering further advancements and innovation in this field.

Higgs and Goldstone modes manifest as fluctuations in the order parameter of system, offering insights into its phase transitions and symmetry properties. Exploring the dynamics of these collective excitations in a Rydberg atoms system advances various branches of condensed matter, particle physics, and cosmology. Here, we report an experimental signature of Higgs and Goldstone modes in a U(1) symmetry-broken Rydberg atomic gases. By constructing two probe fields to excite atoms, we observe the distinct phase and amplitude fluctuations of Rydberg atoms collective excitations under the particle-hole symmetry. Due to the van der Waals interactions between the Rydberg atoms, we detect a symmetric variance spectrum divided by the divergent regime and phase boundary, capturing the full dynamics of the additional Higgs and Goldstone modes. Studying the Higgs and Goldstone modes in Rydberg atoms allows us to explore fundamental aspects of quantum phase transitions and symmetry breaking phenomena, while leveraging the unique properties of these highly interacting systems to uncover new physics and potential applications in quantum simulation.

Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a novel, general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to non-trivial steady states that generalize the notions of communities and structural balance in traditional networks., Comment: 14 pages, 8 figures

Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI., Comment: 6 pages, 2 figures. cs.RO

Study of phase transitions provide insights into how a many-body system behaves under different conditions, enabling us to understand the symmetry breaking, critical phenomena, and topological properties. Strong long-range interactions in highly excited Rydberg atoms create a versatile platform for exploring exotic emergent topological phases. Here, we report the experimental observation of dynamical topological phase transitions in cold Rydberg atomic gases under a microwave field driving. By measuring the system transmission curves while varying the probe intensity, we observe complex hysteresis trajectories characterized by distinct winding numbers as they cross the critical point. At the transition state, where the winding number flips, the topology of these hysteresis trajectories evolves into more non-trivial structures. The topological trajectories are shown to be robust against noise, confirming their rigidity in dynamic conditions. These findings contribute to the insights of emergence of complex dynamical topological phases in many-body systems.

We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity and diversity, our method leverages large-scale web videos accompanied by background music. This enables our model to learn to generate realistic and diverse music. To accomplish this goal, we develop a generative video-music Transformer with a novel semantic video-music alignment scheme. Our model uses a joint autoregressive and contrastive learning objective, which encourages the generation of music aligned with high-level video content. We also introduce a novel video-beat alignment scheme to match the generated music beats with the low-level motions in the video. Lastly, to capture fine-grained visual cues in a video needed for realistic background music generation, we introduce a new temporal video encoder architecture, allowing us to efficiently process videos consisting of many densely sampled frames. We train our framework on our newly curated DISCO-MV dataset, consisting of 2.2M video-music samples, which is orders of magnitude larger than any prior datasets used for video music generation. Our method outperforms existing approaches on the DISCO-MV and MusicCaps datasets according to various music generation evaluation metrics, including human evaluation. Results are available at https://genjib.github.io/project_page/VMAs/index.html, Comment: Project Page: https://genjib.github.io/project_page/VMAs/index.html

Quantum state readout plays a pivotal role in quantum technologies, spanning applications in sensing, computation, and secure communication. In this work, we introduce a new approach for efficiently reading populations of hybrid-spin states in the nitrogen-vacancy center of diamond using a single laser pulse, which utilizes the excited state level anti-crossing mechanism at around 500 Gs. Reading spin state populations through this approach achieves the same outcome as traditional quantum state diagonal tomography but significantly reduces the experimental time by an order of magnitude while maintaining fidelity. Moreover, this approach may be extended to encompass full-state tomography, thereby obviating the requirement for a sequence of spin manipulations and mitigating errors induced by decoherence throughout the procedure.

In recent years, the Substitution-Permutation Network has emerged as a crucial structure for constructing symmetric key ciphers. Composed primarily of linear matrices and nonlinear S-boxes, it offers a robust foundation for cryptographic security. Among the various metrics used to assess the cryptographic properties of linear matrices, the branch number stands out as a particularly important index. Matrices with an optimal branch number are referred to as MDS matrices and are highly prized in the field of cryptography. In this paper we delve into the construction of lightweight MDS matrices. We commence implementation trees of MDS matrices, which is a vital tool for understanding and manipulating their implementations, and then present an algorithm that efficiently enumerates all the lightest MDS matrices based on the word representation. As results, we obtain a series of ultra-lightweight $4\times 4$ MDS matrices, remarkably, 4-bit input MDS matrices with 35 XOR operations and 8-bit input ones with 67 XOR operations . These matrices represent the most comprehensive lightweight MDS matrices available to date. Furthermore, we craft some involution $4\times 4$ MDS matrices with a mere 68 XOR gates.To our best knowledge, they are the best up to date. In the realm of higher-order MDS matrices, we have successfully constructed $5\times 5$ and $6\times 6$ matrices with 114 and 148 XOR gates respectively. These findings outperform the current state-of-the-art.

Cosmic rays (CRs) have strong influences on the chemistry of dense molecular clouds (MCs). To study the detailed chemistry induced by CRs, we conducted a Yebes W band line survey towards an unshocked MC (which we named as 3C391:NML) associated with supernova remnant (SNR) 3C391. We detected emission lines of 18 molecular species in total and estimated their column densities with local thermodynamic equilibrium (LTE) and non-LTE analysis. Using the abundance ratio N(HCO+)/N(CO) and an upper limit of N(DCO+)/N(HCO+), we estimated the CR ionization rate of 3C391:NML is $\zeta\gtrsim 2.7\times 10^{-14}\rm \ s^{-1}$ with an analytic method. However, we caution on adopting this value because chemical equilibrium, which is a prerequisite of using the equations, is not necessarily reached in 3C391:NML. We observed lower N(HCO+)/N(HOC+), higher N(HCS+)/N(CS), and higher X($l$-C3H+) by an order of magnitude in 3C391:NML than the typical values in quiescent dense MCs. We found that an enhanced CR ionization rate (of order $\sim 10^{-15}$ or $\sim 10^{-14}\rm \ s^{-1}$) is needed to reproduce the observation with chemical model. This is higher than the values found in typical MCs by 2--3 orders of magnitude., Comment: 18 pages, 7 figures, accepted for publication in ApJ

Relativistic charged particle beam can be used as destructive beam weapons in space for debris removal tasks. The trajectories of charged particles are affected by both electric and magnetic forces in the Earth's magnetic field. In this paper, we firstly analyzed the correlation parameters of the charged particle beam as a weapon when it propagated in the geomagnetic field. Then the models were constructed based on COMSOL Multiphysics and the IGRF model was adopted in the simulation. The gyro-radius and the related uncertainty were analyzed by simulation of the charged particle transport in the geomagnetic field at different altitudes. The charged beam spot radius divergency was also simulated. The magnetic field pinch effect can be found and can limit the beam spreading., Comment: 10 pages, 7 figures

Polarization beam splitting (BS) has been implemented on gradient metasurface with local response for entanglement manipulation and state reconstruction. To realize more degrees of light modulation, nonlocal modes, manifested as wavelength and momentum selectivity, should be applied into metasurface BS. Here, we demonstrate that single nonlocal phase gradient metasurface(NPGM) can function as a series of independent near-symmetric multiport BS,constructed by its momentum-polarization mode subspaces.Then, using any of above BS with simultaneous multiphoton interference, high-photon NOON states are prepared with high success probability and fidelity. For example,four-mode four-photon NOON state is obtained with 34.8% success probability and fidelity of 99.9%, greatly higher than those previously reported.With unique capability of multiphoton interference, this multiport BS on single NPGM can be directly used in the on-chip quantum photonics. Also, the efficient generation of high-photon NOON states with above BS has potential applications in quantum precision measurement., Comment: 4 figures

Quantum turbulence is a far-from-equilibrium process characterized by high nonlinearity. Holographic duality provides a systematic framework for simulating the decaying $(3+1)$-dimensional quantum turbulence by numerically solving the dual Abelian-Higgs theory in a $(4+1)$-dimensional black hole background. We reveal that different types of total vortex line length $L$ decay behaviors emerge depending on the initial vortex line density, ranging from $L\sim t^{-1.5}$ to $L\sim t^{-1}$, similar to the experimental observation of $^3$He in Phys. Rev. Lett. 96, 035301 (2006). Additionally, by measuring the energy flux at the black hole horizon, we determine that the energy dissipation rate $dE/dt$ is proportional to the square of the total vortex line length, consistent with the vortex line decay equation proposed by W. F. Vinen and also the experimental measurement in Nature Physics 7, 473-476 (2011). We also observe two other characteristics of quantum turbulence: 1) The Kolmogorov $-5/3$ scaling spectrum appears in regions where the total vortex line length decay law is clear and the vortex line density is sufficiently high, while it is less evident in diluted cases; 2) Unlike classical turbulence, the universal power law of superfluid velocity distribution at large speed persists throughout the entire decay process in both types of decay., Comment: 9 pages, 6 figures

The interactions between Rydberg atoms and microwave fields provide a valuable framework for studying the complex dynamics out of equilibrium, exotic phases, and critical phenomena in many-body physics. This unique interplay allows us to explore various regimes of nonlinearity and phase transitions. Here, we observe a phase transition from the state in the regime of bistability to that in multistability in strongly interacting Rydberg atoms by varying the microwave field intensity, accompanying with the breaking of Z3-symmetry. During the phase transition, the system experiences a hidden critical point, in which the multistable states are difficult to be identified. Through changing the initial state of system, we can identify a hidden multistable state and reveal a hidden trajectory of phase transition, allowing us to track to a hidden critical point. In addition, we observe multiple phase transitions in spectra, suggesting higher-order symmetry breaking. The reported results shed light on manipulating multistability in dissipative Rydberg atoms systems and hold promise in the applications of non-equilibrium many-body physics., Comment: 10 pages, 5 figures

We investigate the physical properties of steady flows in a holographic first-order phase transition model, extending from the thermodynamics at equilibrium to the real-time dynamics far from equilibrium. Through spinodal decomposition or condensation nuclei, the phase-separated state with non-zero momentum can be achieved. In this scenario, we observe a gap between coexisting phases, arising not only from the variations in energy density, but also from the distinctions in momentum density or longitudinal pressure. These disparities are characterized by flow velocity and latent heat. Furthermore, by introducing an inhomogeneous scalar external source to simulate a fixed obstacle, we reveal the dynamical response of momentum loss in the moving system. Notably, starting from an initial phase-separated state with uniform flow velocity, and subsequently interacting it with an obstacle, we find that the moving high-energy phase exhibits four characteristic dynamical behaviors -- rebounding, pinning, passing, and splitting. These behaviors depend on the velocity of the phase and the strength of the obstacle.

Chiral nanomaterials offer intriguing possibilities for novel electronic and chemical applications. Here, we report the discovery of an enantiomer-selective magnetoresistance effect in chiral gold nanocrystals. Based on precise control of nanocrystal chiral morphology using amino acid-directed synthesis, we demonstrate that an external magnetic field can dramatically modulate resistance in an enantiomer-specific manner. For a given enantiomer, a magnetic field in one direction alters the resistance by over an order of magnitude, while the opposite field direction leaves it unchanged. This asymmetric response reverses for the opposite enantiomer. We attribute this phenomenon to a novel chirality-driven charge trapping mechanism, where the interplay between the chiral nanocrystal morphology and the magnetic field selectively modifies the surface potential. The magnitude and sign of the magnetoresistance can be further tuned by the surface chemistry of the nanocrystal, as demonstrated through sulfide treatment. Our findings reveal a new form of chirality-dependent magnetoresistance, distinct from previously known effects such as chirality-induced spin selectivity and electric magnetochiral anisotropy. The ability to remotely control surface potentials of chiral nanostructures using magnetic fields could enable novel approaches in catalysis, drug delivery, and nanoelectronics., Comment: 40 pages including all extended data figures

The interplay between strong long-range interactions and the coherent driving contribute to the formation of complex patterns, symmetry, and novel phases of matter in many-body systems. However, long-range interactions may induce an additional dissipation channel, resulting in non-Hermitian many-body dynamics and the emergence of exceptional points in spectrum. Here, we report experimental observation of interaction-induced exceptional points in cold Rydberg atomic gases, revealing the breaking of charge-conjugation parity symmetry. By measuring the transmission spectrum under increasing and decreasing probe intensity, the interaction-induced hysteresis trajectories are observed, which give rise to non-Hermitian dynamics. We record the area enclosed by hysteresis loops and investigate the dynamics of hysteresis loops. The reported exceptional points and hysteresis trajectories in cold Rydberg atomic gases provide valuable insights into the underlying non-Hermitian physics in many-body systems, allowing us to study the interplay between long-range interactions and non-Hermiticity.

The uncertainty principle is deemed as one of cornerstones in quantum mechanics, and exploring its lower limit of uncertainty will be helpful to understand the principle's nature. In this study, we propose a generalized entropic uncertainty relation for arbitrary multiple-observable in multipartite system, and further derive a tighter lower bound by considering Holevo quality and mutual information. Importantly, we detailedly discuss the proposed uncertainty relations and quantum coherence in the context of Schwarzschild space-time. It is interesting to find that Hawking radiation will damage the coherence of the physically accessible region and increase the uncertainty. Furthermore, we argue that the properties of the uncertainty in Schwarzschild space-time can be explained from the systems' purity and the information redistribution of the different regions. Therefore, it is believed that our findings provide the generalized entropic uncertainty relations in multipartite systems, which may facilitate us deeper understanding of quantumness and information paradox of the black holes., Comment: 8 pages, 6 figures, comments are welcome

Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized knowledge in telecom domain. In this paper, for the first time, we propose a pipeline to adapt any general purpose LLMs to a telecom-specific LLMs. We collect and build telecom-specific pre-train dataset, instruction dataset, preference dataset to perform continual pre-training, instruct tuning and alignment tuning respectively. Besides, due to the lack of widely accepted evaluation benchmarks in telecom domain, we extend existing evaluation benchmarks and proposed three new benchmarks, namely, Telecom Math Modeling, Telecom Open QnA and Telecom Code Tasks. These new benchmarks provide a holistic evaluation of the capabilities of LLMs including math modeling, Open-Ended question answering, code generation, infilling, summarization and analysis in telecom domain. Our fine-tuned LLM TelecomGPT outperforms state of the art (SOTA) LLMs including GPT-4, Llama-3 and Mistral in Telecom Math Modeling benchmark significantly and achieve comparable performance in various evaluation benchmarks such as TeleQnA, 3GPP technical documents classification, telecom code summary and generation and infilling., Comment: arXiv admin note: text overlap with arXiv:1303.2654 by other authors

Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have introduced new methodologies and datasets in medical AI. However, the fairness issue under the setting of domain transfer is almost unexplored, while it is common that clinics rely on different imaging technologies (e.g., different retinal imaging modalities) for patient diagnosis. This paper presents FairDomain, a pioneering systemic study into algorithmic fairness under domain shifts, employing state-of-the-art domain adaptation (DA) and generalization (DG) algorithms for both medical segmentation and classification tasks to understand how biases are transferred between different domains. We also introduce a novel plug-and-play fair identity attention (FIA) module that adapts to various DA and DG algorithms to improve fairness by using self-attention to adjust feature importance based on demographic attributes. Additionally, we curate the first fairness-focused dataset with two paired imaging modalities for the same patient cohort on medical segmentation and classification tasks, to rigorously assess fairness in domain-shift scenarios. Excluding the confounding impact of demographic distribution variation between source and target domains will allow clearer quantification of the performance of domain transfer models. Our extensive evaluations reveal that the proposed FIA significantly enhances both model performance accounted for fairness across all domain shift settings (i.e., DA and DG) with respect to different demographics, which outperforms existing methods on both segmentation and classification. The code and data can be accessed at https://ophai.hms.harvard.edu/datasets/harvard-fairdomain20k., Comment: ECCV 2024; Codes and datasets are available at https://github.com/Harvard-Ophthalmology-AI-Lab/FairDomain

In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation. Conventional metrics for assessing SQL code generation, such as matching-based and execution-based methods (e.g., exact set match and execution accuracy), are subject to two primary limitations. Firstly, the former fails to effectively assess functional correctness, as different SQL queries may possess identical functionalities. Secondly, the latter is susceptible to producing false positive samples in evaluations. Our proposed evaluation method, \texttt{FuncEvalGMN}, does not depend on the sufficient preparation of the test data, and it enables precise testing of the functional correctness of the code. Firstly, we parse SQL using a relational operator tree (ROT) called \textit{Relnode}, which contains rich semantic information from the perspective of logical execution.Then, we introduce a GNN-based approach for predicting the functional correctness of generated SQL. This approach incorporates global positional embeddings to address the limitations with the loss of topological information in conventional graph matching frameworks. As an auxiliary contribution, we propose a rule-based matching algorithm, Relnode Partial Matching (\texttt{RelPM}) as a baseline. Finally, we contribute a dataset, \texttt{Pair-Aug-Spider} with a training set and two testing sets, each comprising pairs of SQL codes to simulate various SQL code evaluation scenarios. The training set and one testing dataset focus on code generation using large language models (LLMs), while the other emphasizes SQL equivalence rewriting.

Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems. However, current approaches suffer from suboptimal segmentation performances due to significant challenges in intraoperative images (i.e., low signal-to-noise ratio, small or slender vessels, and strong interference). In this paper, a novel spatial-frequency learning and topological channel interaction network (SPIRONet) is proposed to address the above issues. Specifically, dual encoders are utilized to comprehensively capture local spatial and global frequency vessel features. Then, a cross-attention fusion module is introduced to effectively fuse spatial and frequency features, thereby enhancing feature discriminability. Furthermore, a topological channel interaction module is designed to filter out task-irrelevant responses based on graph neural networks. Extensive experimental results on several challenging datasets (CADSA, CAXF, DCA1, and XCAD) demonstrate state-of-the-art performances of our method. Moreover, the inference speed of SPIRONet is 21 FPS with a 512x512 input size, surpassing clinical real-time requirements (6~12FPS). These promising outcomes indicate SPIRONet's potential for integration into vascular interventional navigation systems. Code is available at https://github.com/Dxhuang-CASIA/SPIRONet.

Multipartite entanglement, linking multiple nodes simultaneously, is a higher-order correlation that offers advantages over pairwise connections in quantum networks (QNs). Creating reliable, large-scale multipartite entanglement requires entanglement routing, a process that combines local, short-distance connections into a long-distance connection, which can be considered as a transformation of network topology. Here, we address the question of whether a QN can be topologically transformed into another via entanglement routing. Our key result is an exact mapping from multipartite entanglement routing to Nash-Williams's graph immersion problem, extended to hypergraphs. This generalized hypergraph immersion problem introduces a partial order between QN topologies, permitting certain topological transformations while precluding others, offering discerning insights into the design and manipulation of higher-order network topologies in QNs., Comment: 11 pages, 6 figures, 1 table