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We report a comprehensive study of electronic band structure for single-layer (SL) and bilayer (BL) RP-nickelates probed by in-situ HP X-ray absorption near edge spectroscopy (XANES). At ambient pressure (AP), the energy splitting delta_E of d_3z^2-r^2 and d_x^2-y^2 bands are directly observed in La3Ni2O7 (BL-La327) but not in La2NiO4 (SL-La214) above E_F, underlining the critical role of inner apical O atoms. A combination of DFT-based electronic band structure and projected density of states (PDOS) calculations with simulated XANES enables us to explain the observed main XANES features labelled by a, A, B', B and C when considering the orbital hybridizations, crystal field splitting (CFS) and core-hole screening of different 3d configurations for SL-La214 and BL-La327 nickelates. At high pressure (HP), the delta_E values of pre-edge peak form a dome-like evolution above 7.7 GPa with the maximum locating at around 20 GPa for metallic BL-La327. Analysis of its integrated area and FWHM provides strong evidence that the bonding d_3z^2-r^2 band crosses E_F above about 7.7 GPa for the metallic BL-La327. Growth of integrated area of pre-edge peak and C peak further evidences pressure-induced hole doping effect. Meanwhile, the pressure dependent FWHM of pre-edge peak implies a nonmonotonic evolution of orbital-selective electronic correlation above 7.7 GPa with extrema emerging at about 20 GPa. Moreover, we estimate the relative hole doping level using the energy shift of pre-edge peak, yielding 0.074 hole per Ni site or equivalently 1.1*10^21 cm^-3 at 20 GPa for the metallic BL-La327, which is comparable to cuprates. Our results have timely examined the electronic band structures as obtained from theoretical calculations, emphasizing the essential role of both d_3z^2-r^2 and d_x^2-y^2 bands as well as the electronic correlation in superconducting pairing for pressurized La3Ni2O7.

Multi-sensor fusion significantly enhances the accuracy and robustness of 3D semantic occupancy prediction, which is crucial for autonomous driving and robotics. However, existing approaches depend on large image resolutions and complex networks to achieve top performance, hindering their application in practical scenarios. Additionally, most multi-sensor fusion approaches focus on improving fusion features while overlooking the exploration of supervision strategies for these features. To this end, we propose DAOcc, a novel multi-sensor fusion occupancy network that leverages 3D object detection supervision to assist in achieving superior performance, while using a deployment-friendly image feature extraction network and practical input image resolution. Furthermore, we introduce a BEV View Range Extension strategy to mitigate the adverse effects of reduced image resolution. As a result, our approach achieves new state-of-the-art results on the Occ3D-nuScenes and SurroundOcc datasets, using ResNet50 and a 256x704 input image resolution. Code will be made available at https://github.com/AlphaPlusTT/DAOcc.

Multimodal Large Language Models (MLLMs) have shown promise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they heavily depend on high-quality data in the Supervised Fine-Tuning (SFT) phase. The existing approaches aim to curate high-quality data via GPT-4V, but they are not scalable due to the commercial nature of GPT-4V and the simplicity of the prompts used to instruct the model. To this end, we devised the FullAnno system, which is a data engine that can generate large-scale, high-quality, and fine-grained image annotations consisting of the category and position of objects, region descriptions, text information, as well as image dense captions. This engine is characterized by its cascade annotation process, which involves multiple expert models and employs rich prompts to instruct LLMs in generating dense image captions. We re-annotated the COCO and Visual Genome datasets using our FullAnno system, tripling the number of object annotations and increasing the length of the original image captions by a factor of 15. Experiments show that the regenerated annotation can significantly enhance the capabilities of LLaVA-v1.5 on several benchmarks. The re-annotated data are available at: https://arcana-project-page.github.io, Comment: 7 pages, 5 figures, 2 tables

Fusion plasma is a typical non-equilibrium and nonlinear system, with the interaction between different species well described by the Vlasov-Fokker-Planck (VFP) equations. The transport of mass, momentum, energy, and temperature relaxation are important issues, which are affected by the collision term of VFP even in so-called collision-less plasma domain. Hence, nonlinearity and collisions are crucial features in large regime. A successful numerical simulation for non-equilibrium plasma has to be able to conserve mass, momentum and energy, while to satisfy Boltzmann's H-theorem and higher-order moment convergence. An expansion of the distribution function in spherical harmonics (Legendre basis when the velocity space exhibits axisymmetry) in angle coordinate and in King basis in speed coordinate of velocity space is well suited to address these requirements. This paper reviews the formulation of the 0D-2V VFP equation in terms of spherical harmonics coupled with King function and its solution in our NLVFP code. In this topic review, we will introduce the background physics related to the nonlinear VFP simulation, then describe NLVFP for 0D-2V homogeneous, weakly anisotropic plasma with utilization of the Shkarofsky's form of Fokker-Planck-Rosenbluth (FPRS) collision operator., Comment: arXiv admin note: text overlap with arXiv:2409.10060

In this paper, we establish the central limit theorem (CLT) for the linear spectral statistics (LSS) of sample correlation matrix $R$, constructed from a $p\times n$ data matrix $X$ with independent and identically distributed (i.i.d.) entries having mean zero, variance one, and infinite fourth moments in the high-dimensional regime $n/p\rightarrow \phi\in \mathbb{R}_+\backslash \{1\}$. We derive a necessary and sufficient condition for the CLT. More precisely, under the assumption that the identical distribution $\xi$ of the entries in $X$ satisfies $\mathbb{P}(|\xi|>x)\sim l(x)x^{-\alpha}$ when $x\rightarrow \infty$ for $\alpha \in (2,4]$, where $l(x)$ is a slowly varying function, we conclude that: (i). When $\alpha\in(3,4]$, the universal asymptotic normality for the LSS of sample correlation matrix holds, with the same asymptotic mean and variance as in the finite fourth moment scenario; (ii) We identify a necessary and sufficient condition $\lim_{x\rightarrow\infty}x^3\mathbb{P}(|\xi|>x)=0$ for the universal CLT; (iii) We establish a local law for $\alpha \in (2, 4]$. Overall, our proof strategy follows the routine of the matrix resampling, intermediate local law, Green function comparison, and characteristic function estimation. In various parts of the proof, we are required to come up with new approaches and ideas to solve the challenges posed by the special structure of sample correlation matrix. Our results also demonstrate that the symmetry condition is unnecessary for the CLT of LSS for sample correlation matrix, but the tail index $\alpha$ plays a crucial role in determining the asymptotic behaviors of LSS for $\alpha \in (2, 3)$., Comment: 112 pages

A kinetic moment-closed model, derived from the Vlasov-Fokker-Planck equation for homogeneous plasma with spherically symmetric velocity space, is introduced as a general relaxation model. The closed form of this nonlinear model is presented by introducing a set new functions called R function and R integration. This model, based on the finitely distinguishable independent features hypothesis, allows for capturing the nature of equilibrium state. From this relaxation model, a general temperature relaxation model is derived and the general characteristic frequency of temperature relaxation when velocity space exhibits spherical symmetry is presented., Comment: 16 pages, 2 figures. arXiv admin note: text overlap with arXiv:2408.01613

Currently, vision encoder models like Vision Transformers (ViTs) typically excel at image recognition tasks but cannot simultaneously support text recognition like human visual recognition. To address this limitation, we propose UNIT, a novel training framework aimed at UNifying Image and Text recognition within a single model. Starting with a vision encoder pre-trained with image recognition tasks, UNIT introduces a lightweight language decoder for predicting text outputs and a lightweight vision decoder to prevent catastrophic forgetting of the original image encoding capabilities. The training process comprises two stages: intra-scale pretraining and inter-scale finetuning. During intra-scale pretraining, UNIT learns unified representations from multi-scale inputs, where images and documents are at their commonly used resolution, to enable fundamental recognition capability. In the inter-scale finetuning stage, the model introduces scale-exchanged data, featuring images and documents at resolutions different from the most commonly used ones, to enhance its scale robustness. Notably, UNIT retains the original vision encoder architecture, making it cost-free in terms of inference and deployment. Experiments across multiple benchmarks confirm that our method significantly outperforms existing methods on document-related tasks (e.g., OCR and DocQA) while maintaining the performances on natural images, demonstrating its ability to substantially enhance text recognition without compromising its core image recognition capabilities.

In this account, we will give an overview of our research progress on novel quantum properties in topological quantum materials with kagome lattice. Here, there are mainly two categories of kagome materials: magnetic kagome materials and nonmagnetic ones. On one hand, magnetic kagome materials mainly focus on the 3d transition-metal-based kagome systems, including Fe$_3$Sn$_2$, Co$_3$Sn$_2$S$_2$, YMn6Sn6, FeSn, and CoSn. The interplay between magnetism and topological bands manifests vital influence on the electronic response. For example, the existence of massive Dirac or Weyl fermions near the Fermi level signicantly enhances the magnitude of Berry curvature in momentum space, leading to a large intrinsic anomalous Hall effect. In addition, the peculiar frustrated structure of kagome materials enables them to host a topologically protected skyrmion lattice or noncoplaner spin texture, yielding a topological Hall effect that arises from the realspace Berry phase. On the other hand, nonmagnetic kagome materials in the absence of longrange magnetic order include CsV3Sb5 with the coexistence of superconductivity, charge density wave state, and band topology and van der Waals semiconductor Pd$_3$P$_2$S$_8$. For these two kagome materials, the tunability of electric response in terms of high pressure or carrier doping helps to reveal the interplay between electronic correlation effects and band topology and discover the novel emergent quantum phenomena in kagome materials., Comment: 10 pages,7 figures

The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on training free-based methods (e.g., image inversion) due to the scarcity of specific data. In this study, we present a data construction pipeline for content-style-stylized image triplets that generates and automatically cleanses stylized data triplets. Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale style transfer dataset containing 210k image triplets, available for the community to explore and research. Equipped with IMAGStyle, we propose CSGO, a style transfer model based on end-to-end training, which explicitly decouples content and style features employing independent feature injection. The unified CSGO implements image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis. Extensive experiments demonstrate the effectiveness of our approach in enhancing style control capabilities in image generation. Additional visualization and access to the source code can be located on the project page: \url{https://csgo-gen.github.io/}.

Unsupervised person re-identification (Re-ID) aims to learn a feature network with cross-camera retrieval capability in unlabelled datasets. Although the pseudo-label based methods have achieved great progress in Re-ID, their performance in the complex scenario still needs to sharpen up. In order to reduce potential misguidance, including feature bias, noise pseudo-labels and invalid hard samples, accumulated during the learning process, in this pa per, a confidence-guided clustering and contrastive learning (3C) framework is proposed for unsupervised person Re-ID. This 3C framework presents three confidence degrees. i) In the clustering stage, the confidence of the discrepancy between samples and clusters is proposed to implement a harmonic discrepancy clustering algorithm (HDC). ii) In the forward-propagation training stage, the confidence of the camera diversity of a cluster is evaluated via a novel camera information entropy (CIE). Then, the clusters with high CIE values will play leading roles in training the model. iii) In the back-propagation training stage, the confidence of the hard sample in each cluster is designed and further used in a confidence integrated harmonic discrepancy (CHD), to select the informative sample for updating the memory in contrastive learning. Extensive experiments on three popular Re-ID benchmarks demonstrate the superiority of the proposed framework. Particularly, the 3C framework achieves state-of-the-art results: 86.7%/94.7%, 45.3%/73.1% and 47.1%/90.6% in terms of mAP/Rank-1 accuracy on Market-1501, the com plex datasets MSMT17 and VeRi-776, respectively. Code is available at https://github.com/stone5265/3C-reid.

Most LiDAR odometry and SLAM systems construct maps in point clouds, which are discrete and sparse when zoomed in, making them not directly suitable for navigation. Mesh maps represent a dense and continuous map format with low memory consumption, which can approximate complex structures with simple elements, attracting significant attention of researchers in recent years. However, most implementations operate under a static environment assumption. In effect, moving objects cause ghosting, potentially degrading the quality of meshing. To address these issues, we propose a plug-and-play meshing module adapting to dynamic environments, which can easily integrate with various LiDAR odometry to generally improve the pose estimation accuracy of odometry. In our meshing module, a novel two-stage coarse-to-fine dynamic removal method is designed to effectively filter dynamic objects, generating consistent, accurate, and dense mesh maps. To our best know, this is the first mesh construction method with explicit dynamic removal. Additionally, conducive to Gaussian process in mesh construction, sliding window-based keyframe aggregation and adaptive downsampling strategies are used to ensure the uniformity of point cloud. We evaluate the localization and mapping accuracy on five publicly available datasets. Both qualitative and quantitative results demonstrate the superiority of our method compared with the state-of-the-art algorithms. The code and introduction video are publicly available at https://yaepiii.github.io/CAD-Mesher/., Comment: 9 pages, 7 figures

In this study, we present an optimal implicit algorithm specifically designed to accurately solve the multi-species nonlinear 0D-2V axisymmetric Fokker-Planck-Rosenbluth (FPR) collision equation while preserving mass, momentum, and energy. Our approach relies on the utilization of nonlinear Shkarofsky's formula of FPR (FPRS) collision operator in terms of Legendre polynomial expansions. The key innovation lies in the introduction of a new function named King (Eq.(54)) with the adoption of the Legendre polynomial expansion for the angular direction and King function expansion for the velocity axis direction. The Legendre polynomial expansion will converge exponentially and the King method, a moment convergence algorithm, could ensure the conservation with high precision in discrete form. Additionally, a post-step projection to manifolds is employed to exactly enforce symmetries of the collision operators. Through solving several typical problems across various nonequilibrium configurations, we demonstrate the superior performance and high accuracy of our algorithm., Comment: 43 Page, 26 Figures

We derive the transport equations from the Vlasov-Fokker-Planck equation when the velocity space is spherically symmetric. The Shkarofsky's form of Fokker-Planck-Rosenbluth collision operator is employed in the Vlasov-Fokker-Planck equation. A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric2F1 functions. This has been accomplished based on the Maxwellian mixture model. Furthermore, we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived. The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features, without relying on the conventional near-equilibrium assumption., Comment: 15 pages, 1 figure

Learning object-centric representations from unsupervised videos is challenging. Unlike most previous approaches that focus on decomposing 2D images, we present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning within a differentiable volume rendering framework. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene, which infers per-object occupancy probabilities at individual spatial locations. These voxel features evolve through a canonical-space deformation function and are optimized in an inverse rendering pipeline with a compositional NeRF. Additionally, our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids. DynaVol-S significantly outperforms existing models in both novel view synthesis and unsupervised decomposition tasks for dynamic scenes. By jointly considering geometric structures and semantic features, it effectively addresses challenging real-world scenarios involving complex object interactions. Furthermore, once trained, the explicitly meaningful voxel features enable additional capabilities that 2D scene decomposition methods cannot achieve, such as novel scene generation through editing geometric shapes or manipulating the motion trajectories of objects.

A general framework for studying McKean-Vlasov SDEs via monotone dynamical systems is established in this paper. Under a cooperative condition, we show McKean-Vlasov SDEs admit a comparison principle with respect to the stochastic order, and generate monotone dynamical systems on the $2$-Wasserstein space. Our main results prove the existence of unstable invariant measures, total orderedness of invariant measures, and the existence of monotone connecting orbits between order-related invariant measures for general cooperative McKean-Vlasov SDEs. To achieve our goals, we adopt the theory of monotone dynamical systems, extend the connecting orbit theorem, and deduce a dichotomy structure of equilibria. This method is different from existing approaches, like propagation of chaos and Fokker-Planck equations. A wide range of classical examples are covered by our framework, such as granular media equations in double-well and multi-well confinement potentials with quadratic interaction, double-well landscapes with perturbation, and higher dimensional equations, even driven by multiplicative noises., Comment: 56 pages, 2 figures

High-temperature superconductivity (HTSC) remains one of the most challenging and fascinating mysteries in condensed matter physics. Recently, superconductivity with transition temperature exceeding liquid-nitrogen temperature is discovered in La3Ni2O7 at high pressure, which provides a new platform to explore the unconventional HTSC. In this work, using high-resolution angle-resolved photoemission spectroscopy and ab-initio calculation, we systematically investigate the electronic structures of La3Ni2O7 at ambient pressure. Our experiments are in nice agreement with ab-initio calculations after considering an orbital-dependent band renormalization effect. The strong electron correlation effect pushes a flat band of d_(z^2 ) orbital component below the Fermi level (EF), which is predicted to locate right at EF under high pressure. Moreover, the d_(x^2-y^2 ) band shows a pseudogap-like behavior with suppressed spectral weight and diminished quasiparticle peak near EF. Our findings provide important insights into the electronic structure of La3Ni2O7, which will shed light on the understanding of the unconventional superconductivity in nickelates.

Strategies to achieve higher upper-critical-field superconductors ({\mu}0Hc2(0)) are of great interest for both fundamental science and practical applications. While reducing the thickness of two-dimensional (2D) materials to a few layers significantly enhances {\mu}0Hc2(0) with accompanied potential unconventional pairing mechanisms, further dimensional reduction to 1D compounds rarely exceeds the expected Pauli limit. Here, we report the discovery of a 1D granular Ta-Te nanowire that becomes superconducting under high pressure, with a maximum critical temperature (Tc) of 5.1 K. Remarkably, the {\mu}0Hc2(0) reaches 16 T, which is twice the Pauli limit, setting a record of {\mu}0Hc2 (0) in all the reported 1D superconductors. Our work demonstrates that the Ta-Te nanowire not only is a potential candidate for applications in high magnetic fields, but also provides an ideal platform for further investigations of the mechanisms between nanowires and large {\mu}0Hc2(0)., Comment: 12 pages,4 figures

Interactions among various electronic states such as CDW, magnetism, and superconductivity are of high significance in strongly correlated systems. While significant progress has been made in understanding the relationship between CDW and superconductivity, the interplay between CDW and magnetic order remains largely elusive. Kagome lattices, which intertwine nontrivial topology, charge order, and magnetism, offer an ideal platform for such studies. The kagome magnet FeGe, hosting the unique coupling between CDW and magnetism, has recently garnered considerable attention in that respect. Here we reveal the significant role of the orbital coupling effect during the CDW phase transition, highlighting the orbital origin of the magnetic moment enhancement in FeGe. Our X ray absorption experiments and first principles calculations illuminate the temperature dependent behavior of Fe3d_Ge4p orbital hybridization and corroborate its pivotal impact on the magnetic properties of FeGe. These findings introduce an orbital dimension to the correlation between charge and magnetic degrees of freedom, advancing our understanding of the intriguing quantum phases resulting from this interplay.

Recently, many methods have been developed to extend the context length of pre-trained large language models (LLMs), but they often require fine-tuning at the target length ($\gg4K$) and struggle to effectively utilize information from the middle part of the context. To address these issues, we propose $\textbf{C}$ontinuity-$\textbf{R}$elativity ind$\textbf{E}$xing with g$\textbf{A}$ussian $\textbf{M}$iddle ($\texttt{CREAM}$), which interpolates positional encodings by manipulating position indices. Apart from being simple, $\texttt{CREAM}$ is training-efficient: it only requires fine-tuning at the pre-trained context window (e.g., Llama 2-4K) and can extend LLMs to a much longer target context length (e.g., 256K). To ensure that the model focuses more on the information in the middle, we introduce a truncated Gaussian to encourage sampling from the middle part of the context during fine-tuning, thus alleviating the "Lost-in-the-Middle" problem faced by long-context LLMs. Experimental results show that $\texttt{CREAM}$ successfully extends LLMs to the target length for both Base and Chat versions of $\texttt{Llama2-7B}$ with "Never Miss A Beat". Our code is publicly available at https://github.com/bigai-nlco/cream., Comment: Accepted by NeurIPS 2024

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, health-care, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these digital services, whether due to human or technical failures, natural disasters, or terrorism, would significantly impact citizens' daily lives. Hence, 6G networks need not only to provide high-performance services but also to be resilient in maintaining essential services in the face of potentially unknown challenges. This paper provides a general review of the state of the art on resilient systems, definitions, concepts, and approaches. Moreover, it introduces a comprehensive concept, i.e., resilience-by-design (RBD), in three different levels for designing resilient 6G communication networks, summarizing our initial studies within the German Open6GHub project. First, we outline the general RBD enabling principles and discuss their related sub-categories. Next, adopting an interdisciplinary approach, we propose to embed these principles across all 6G layers/perspectives including electronics, physical channel, network components and functions, networks, services, and cross-layer and cross-infrastructure considerations and discuss their challenges. We further elaborate the RBD principles and their realizations along with several 6G use-cases. The paper is concluded by presenting a comprehensive list of open problems for future research on 6G resilience.

As an empirical tool in materials science and engineering, the iconic phase diagram owes its robustness and practicality to the topological characteristics rooted in the celebrated Gibbs phase law (F = C - P + 2). When crossing the phase diagram boundary, the structure transition occurs abruptly, bringing about an instantaneous change in physical properties and limited controllability on the boundaries (F = 1). Here, we expand the sharp phase boundary to an amorphous transition region (F = 2) by partially disrupting the long-range translational symmetry, leading to a sequential crystalline-amorphous-crystalline (CAC) transition in a pressurized In2Te5 single crystal. Through detailed in-situ synchrotron diffraction, we elucidate that the phase transition stems from the rotation of immobile blocks [In2Te2]2+, linked by hinge-like [Te3]2- trimers. Remarkably, within the amorphous region, the amorphous phase demonstrates a notable 25 % increase of the superconducting transition temperature (Tc), while the carrier concentration remains relatively constant. Furthermore, we propose a theoretical framework revealing that the unconventional boost in amorphous superconductivity might be attributed to an intensified electron correlation, triggered by a disorder-augmented multifractal behavior. These findings underscore the potential of disorder and prompt further exploration of unforeseen phenomena on the phase boundaries., Comment: 14 pages, 4 figures, Accepted for publication in Advanced Materials

Weyl semimetals with magnetic ordering provide a promising platform for the investigation of rare topological effects such as the anomalous Hall effect, resulting from the interplay of nontrivial bands with various spin configurations. The materials $R$AlSi, where $R$ represents a rare-earth element, are prominent representatives of Weyl semimetals, where the Weyl states are induced by space inversion symmetry breaking and in addition, for several rare-earth elements $R$, enhanced by time-reversal symmetry breaking through the formation of a magnetic order at low temperature. We report optical signatures of Weyl fermions in the magnetic compounds CeAlSi, PrAlSi, NdAlSi, and SmAlSi as well as the non-magnetic family member LaAlSi by broad-frequency infrared spectroscopy at room temperature, i.e., in the paramagnetic phase. A similar profile of the optical conductivity spectrum and a metallic character are observed for all compounds, with LaAlSi showing the strongest free charge carrier contribution. Furthermore, the linear-in-frequency behavior of the optical conductivity of all investigated compounds indicates the presence of Weyl nodes in close vicinity of the Fermi energy, resulting from inversion symmetry breaking in noncentrosymmetric structures. According to the characteristics of these linear slopes, the $R$AlSi compounds are expected to host mainly type-II Weyl states with overtilted Weyl cones. The results are compared to the optical response of the closely related $R$AlGe materials, which are considered as potential hybridization-driven Weyl-Kondo systems., Comment: 8 pages, 6 figures. Accepted for publication in Phys. Rev. B

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques, integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162,605 nodes and 731,772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated science knowledge graphs., Comment: 13 pages, 7 figures, 3 tables

Strongly correlated electron systems with a kagome lattice can host abundant exotic quantum states such as superconductivity and spin/charge density waves (CDW) due to the complicated interactions between different degrees of freedoms in the framework of a unique two-dimensional geometrically frustrated lattice structure. Recently, successive orders of A-type antiferromagnetism (AFM), $2\times2\times2$ CDW and canted double-cone AFM have been manifested upon cooling in magnetic kagome FeGe. However, the mechanism of the CDW order and its interaction with magnetism are presently enigmatic at best. Here we investigate the evolution of CDW order with temperature across the spin canting transition in FeGe by single-crystal x-ray diffraction. Refinements of its modulated structure are presented using the superspace approach. Interestingly, the superlattice reflections originating from CDW-induced long-range structural modulation become extremely weak after the system enters the canted AFM while a $2\times2$ CDW in the $ab$ plane persists as a long-range order demonstrated by strong electronic modulation in the d$I$/d$V$ map of scanning tunneling spectroscopy. We discovered a novel CDW order without long-range structural modulation in FeGe probably because of the competition between CDW and canted AFM in determining the underlying crystal structure. In addition, occupational modulations of Ge1 atoms located in the kagome plane and displacive modulations of all the atoms were extracted from the refinements, confirming the existence of Ge atom dimerization along the $c$ axis as the major distortion and indicating a dynamic transformation between different CDW domains., Comment: 22 pages, 6 figures. Comments on the manuscript are welcome

We report on the charge dynamics of kagome FeGe, an antiferromagnet with a charge density wave (CDW) transition at $T_{\mathrm{CDW}} \simeq 105$ K, using polarized infrared spectroscopy and band structure calculations. We reveal a pronounced optical anisotropy, various excitations associated with flat bands and van Hove singularities (VHSs), and a moderate level of electronic correlations. Notably, there are two types of remarkable spectral weight (SW) redistributions for above and below $T_{\mathrm{CDW}}$. The former involves a transfer between incoherent and coherent excitations driven by the magnetic splitting-induced elevation of flat bands. The latter manifests itself as a sudden change of SW from low to high energies for both $a$ and $c$ directions, suggesting a first-order transition and the three-dimensional nature of CDW. These anomalies in SW significantly differ from those observed in other kagome metals like CsV$_3$Sb$_5$, where the nesting of VHSs results in a pronounced CDW gap feature. Instead, our findings can be accounted for by the jump of VHSs relative to the Fermi energy via a first-order structural transition involving large partial Ge1-dimerization. Our study thus unveils a complex interplay among structure, magnetism, electronic correlations, and charge order in FeGe, offering valuable insights for a comprehensive understanding of CDW order in kagome systems., Comment: 7 pages, 3 figures

Layered materials with kagome lattice have attracted a lot of attention due to the presence of nontrivial topological bands and correlated electronic states with tunability. In this work, we investigate a unique van der Waals (vdW) material system, $A_{2}M_{3}X_{4}$ ($A$ = K, Rb, Cs; $M$ = Ni, Pd; $X$ = S, Se), where transition metal kagome lattices, chalcogen honeycomb lattices and alkali metal triangular lattices coexist simultaneously. A notable feature of this material is that each Ni/Pd atom is positioned in the center of four chalcogen atoms, forming a local square-planar environment. This crystal field environment results in a low spin state $S$ = 0 of Ni$^{2+}$/Pd$^{2+}$. A systematic study of the crystal growth, crystal structure, magnetic and transport properties of two representative compounds, Rb$_{2}$Ni$_{3}$S$_{4}$ and Cs$_{2}$Ni$_{3}$Se$_{4}$, has been carried out on powder and single crystal samples. Both compounds exhibit nonmagnetic $p$-type semiconducting behavior, closely related to the particular chemical environment of Ni$^{2+}$ ions and the alkali metal intercalated vdW structure. Additionally, Cs$_{2}$Ni$_{3}$Se$_{4}$ undergoes an insulator-metal transition (IMT) in transport measurements under pressure up to 87.10 GPa without any structural phase transition, while Rb$_{2}$Ni$_{3}$S$_{4}$ persists in its semiconducting behavior., Comment: 9 pages, 6 figures

In the realm of ferroelectric memories, HfO2-based ferroelectrics stand out because of their exceptional CMOS compatibility and scalability. Nevertheless, their switchable polarization and switching speed are not on par with those of perovskite ferroelectrics. It is widely acknowledged that defects play a crucial role in stabilizing the metastable polar phase of HfO2. Simultaneously, defects also pin the domain walls and impede the switching process, ultimately rendering the sluggish switching of HfO2. Herein, we present an effective strategy involving acceptor-donor co-doping to effectively tackle this dilemma. Remarkably enhanced ferroelectricity and the fastest switching process ever reported among HfO2 polar devices are observed in La3+-Ta5+ co-doped HfO2 ultrathin films. Moreover, robust macro-electrical characteristics of co-doped films persist even at a thickness as low as 3 nm, expanding potential applications of HfO2 in ultrathin devices. Our systematic investigations further demonstrate that synergistic effects of uniform microstructure and smaller switching barrier introduced by co-doping ensure the enhanced ferroelectricity and shortened switching time. The co-doping strategy offers an effective avenue to control the defect state and improve the ferroelectric properties of HfO2 films.

By using low-temperature scanning tunneling microscopy and spectroscopy (STM/STS), we observe in-gap states induced by Andreev tunneling through a single impurity state in a low carrier density superconductor (NaAlSi). The energy-symmetric in-gap states appear when the impurity state is located within the superconducting gap. In-gap states can cross the Fermi level, and they show X-shaped spatial variation. We interpret the in-gap states as a consequence of the Andreev tunneling through the impurity state, which involves the formation or breakup of a Cooper pair. Due to the low carrier density in NaAlSi, the in-gap state is tunable by controlling the STM tip-sample distance. Under strong external magnetic fields, the impurity state shows Zeeman splitting when it is located near the Fermi level. Our findings not only demonstrate the Andreev tunneling involving single electronic state, but also provide new insights for understanding the spatially-dependent in-gap states in low carrier density superconductors., Comment: 12 pages, 4 figures

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $\phi_m$. However, actual adoption of the information only occurs when the cumulative influence across all communication channels exceeds a second threshold, $\phi_k$. We analytically derive the cascade condition for both the case of a single seed node using percolation methods and the case of any seed size employing mean-field approximation. Our findings underscore that when considering the fractional seed size, $r_0 \in (0,1]$, the connectivity pattern of the random hypergraph, characterized by the hyperdegree, $k$, and cardinality, $m$, distributions, exerts an asymmetric impact on the global cascade boundary. This asymmetry manifests in the observed differences in the boundaries of the global cascade within the $(\phi_m, \langle m \rangle)$ and $(\phi_k, \langle k \rangle)$ planes. However, as $r_0 \to 0$, this asymmetric effect gradually diminishes. Overall, by elucidating the mechanisms driving information cascades within a broader context of higher-order interactions, our research contributes to theoretical advancements in complex systems theory., Comment: 11 pages, 10 figures

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images, including \textbf{point}, \textbf{box}, \textbf{scribble}, and \textbf{mask}. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths, thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM, the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM, we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably, VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore, VRP-SAM demonstrates strong generalization capabilities, allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at \url{https://github.com/syp2ysy/VRP-SAM}, Comment: Accepted by CVPR 2024; The camera-ready version

3D Shape represented as point cloud has achieve advancements in multimodal pre-training to align image and language descriptions, which is curial to object identification, classification, and retrieval. However, the discrete representations of point cloud lost the object's surface shape information and creates a gap between rendering results and 2D correspondences. To address this problem, we propose GS-CLIP for the first attempt to introduce 3DGS (3D Gaussian Splatting) into multimodal pre-training to enhance 3D representation. GS-CLIP leverages a pre-trained vision-language model for a learned common visual and textual space on massive real world image-text pairs and then learns a 3D Encoder for aligning 3DGS optimized per object. Additionally, a novel Gaussian-Aware Fusion is proposed to extract and fuse global explicit feature. As a general framework for language-image-3D pre-training, GS-CLIP is agnostic to 3D backbone networks. Experiments on challenging shows that GS-CLIP significantly improves the state-of-the-art, outperforming the previously best results., Comment: The content of the technical report needs to be updated and retracted to avoid other impacts

Intelligent machines (IMs), including industrial machines, unmanned aerial vehicles (UAVs), and unmanned vehicles, etc., could perform effective cooperation in complex environment when they form IM network. The efficient environment sensing and communication are crucial for IM network, enabling the real-time and stable control of IMs. With the emergence of integrated sensing and communication (ISAC) technology, IM network is empowered with ubiquitous sensing capabilities, which is helpful in improving the efficiency of communication and sensing with the mutual benefit of them. However, the massive amount of sensing information brings challenges for the processing, storage and application of sensing information. In this article, ISAC driven digital twin (DT) is proposed for IM network, and the architecture and enabling technologies are revealed. ISAC driven DT structurally stores the sensing information, which is further applied to optimize communication, networking and control schemes of IMs, promoting the widespread applications of IMs., Comment: 9 pages, 5 figures, 1 Table

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge lies in managing communication among diverse devices with different protocols. To address these issues, a comprehensive framework is designed and implemented to facilitate prosumers' integration in flexibility strategies, addressing communication at various levels. The effectiveness of the proposed framework is demonstrated through its implementation in a real smart home environment with diverse devices. The framework enables seamless integration and communication between IoT devices and IEC 61,850-compliant power devices. This research presents a novel approach to address the challenges of managing flexibility strategies in residential areas, providing a practical solution for prosumers to actively participate in optimizing energy consumption and enhancing the stability and quality of the electricity system amidst the growing integration of distributed renewable energy.

Mediated by the interaction with quantum vacuum fields, a laser field propagating in a nonlinear optical medium can generate new light fields via spontaneous parametric process. Such process is inherent independent of the propagation direction of light and reciprocal thus far, due to the direction-independent field-vacuum interaction. In this work, we experimentally demonstrate a nonreciprocal spontaneous parametric four-wave mixing process in sodium atomic vapors with dispersive nonlinearity and further broadband optical isolation by unidirectionally coupling the probe field to an auxiliary quantum vacuum field in another four-wave mixing process. Thanks to the broad bandwidth of the spontaneous parametric process, in combination with the Doppler and power-induced broadening of atomic energy levels, we achieve optical isolation with a bandwidth larger than 100 GHz for isolation ratio >25 dB. Considering that both spontaneous parametric processes and wave mixing in nonlinear medium have been realized in diverse on-chip photonic platforms, our work paves the way for integrated broadband optical isolations and thus can boost scalability and function of photonic chips., Comment: 17 pages, 4 figures

Enhancing the energy efficiency of devices stands as one of the key requirements in the fifth-generation (5G) cellular network and its evolutions toward the next generation wireless technology. Specifically, for battery-limited Internet-of-Things (IoT) devices where downlink monitoring significantly contributes to energy consumption, efficient solutions are required for power saving while addressing performance tradeoffs. In this regard, the use of a low-power wake-up receiver (WUR) and wake-up signal (WUS) is an attractive solution for reducing the energy consumption of devices without compromising the downlink latency. This paper provides an overview of the standardization study on the design of low-power WUR and WUS within Release 18 of the third-generation partnership project (3GPP). We describe design principles, receiver architectures, waveform characteristics, and device procedures upon detection of WUS. In addition, we provide representative results to show the performance of the WUR in terms of power saving, coverage, and network overhead along with highlighting design tradeoffs.

The (Bi2)m(Bi2Te3)n homologous series possess natural multilayer heterostructure with intriguing physical properties at ambient pressure. Herein, we report the pressure-dependent evolution of the structure and electrical transport of the dual topological insulator BiTe, a member of the (Bi2)m(Bi2Te3)n series. With applied pressure, BiTe exhibits several different crystal structures and distinct superconducting states, which is remarkably similar to other members of the (Bi2)m(Bi2Te3)n series. Our results provide a systematic phase diagram for the pressure-induced superconductivity in BiTe, contributing to the highly interesting physics in this (Bi2)m(Bi2Te3)n series., Comment: 17 pages, 5 figures

Binary metal sulfides are potential material family for exploring high Tc superconductors under high pressure. In this work, we study the crystal structures, electronic structures and superconducting properties of the Lu-S system in the pressure range from 0 GPa to 200 GPa, combining crystal structure predictions with ab-initio calculations. We predict 14 new structures, encompassing 7 unidentified stoichiometries. Within the S-rich structures, the formation of S atom cages is beneficial for superconductivity, with the superconducting transition temperature 25.86 K and 25.30 K for LuS6-C2/m at 70 GPa and LuS6-R-3m at 90 GPa, respectively. With the Lu/(Lu+S) ratio increases, the Lu-d electrons participate more in the electronic properties at the Fermi energy, resulting in the coexistence of superconductivity and topological non-triviality of LuS2-Cmca, as well as the superconductivity of predicted Lu-rich compounds. Our calculation is helpful for understanding the exotic properties in transition metal sulfides system under high pressure, providing possibility in designing novel superconductors for future experimental and theoretical works., Comment: 17 pages,8 figures

Tellurium (Te) is one of the p-orbital chalcogens, which shows attractive physical properties at ambient pressure. Here, we systematically investigate both structural and electronic evolution of Te single crystal under high pressure up to 40 GPa. The pressure dependence of the experimental Raman spectrum reveals the occurrence of multiple phase transitions, which is consistent with highpressure synchrotron X-ray diffraction (XRD) measurements. The appearance of superconductivity in high-pressure phase of Te is accompanied by structural phase transitions. The high-pressure phases of Te reveal a nonmonotonic evolution of superconducting temperature Tc with notably different upper critical fields. The theoretical calculations demonstrate that the pressure dependence of the density of states(DOS) agrees well with the variation of Tc. Our results provide a systematic phase diagram for the pressure-induced superconductivity of Te., Comment: 9 pages, 6 figures

The research on hydrogen-rich ternary compounds attract tremendous attention for it paves new route to room-temperature superconductivity at lower pressures. Here, we study the crystal structures, electronic structures, and superconducting properties of the ternary Ca-U-H system, combining crystal structure predictions with ab-initio calculations under high pressure. We found four dynamically stable structures with hydrogen clathrate cages: CaUH12-Cmmm, CaUH12-Fd-3m, Ca2UH18-P-3m1, and CaU3H32-Pm-3m. Among them, the Ca2UH18-P-3m1 and CaU3H32-Pm-3m are likely to be synthesized below 1 megabar. The f electrons in U atoms make dominant contribution to the electronic density of states around the Fermi energy. The electron-phonon interaction calculations reveal that phonon softening in the mid-frequency region can enhance the electron-phonon coupling significantly. The Tc value of Ca2UH18-P-3m1 is estimated to be 57.5-65.8 K at 100 GPa. Our studies demonstrate that introducing actinides into alkaline-earth metal hydrides provides possibility in designing novel superconducting ternary hydrides., Comment: 13 pages,4 figures

Quasicrystals show long-range order, but lack translational symmetry. So far, theoretical and experimental studies suggest that both Hermitian and non-Hermitian quasicrystals show localized eigenstates. This localization is due to the fractal structure of the spectrum in the Hermitian case and to the transition to diffusive bands via exceptional points in the non-Hermitian case. Here, we present an experimental study of a dodecagonal (12-fold) photonic quasicrystal based on electromagnetically-induced transparency in a Rb vapor cell. The transition to a quasicrystal is obtained by superposing two honeycomb lattices at 30$^\circ$ with a continuous tuning of their amplitudes. Non-Hermiticity is controlled independently. We study the spatial expansion of a probe wavepacket. In the Hermitian case, the wavepacket expansion is suppressed when the amplitude of the second lattice is increased (quasicrystal localization). We find a new regime, where increasing the non-Hermitian potential in the quasicrystal enhances spatial expansion, with the $C_{12}$ symmetry becoming visible in the wavepacket structure. This real-space expansion is due to a k-space localization on specific quasicrystal modes. Our results show that the non-Hermitian quasicrystal behavior is richer than previously thought. The localization properties of the quasicrystals can be used for beam tailoring in photonics, but are also important in other fields.

Non-Gaussian entangled states play a crucial role in harnessing quantum advantage in continuous-variable quantum information. However, how to fully characterize N-partite (N > 3) non-Gaussian entanglement without quantum state tomography remains elusive, leading to a very limited understanding of the underlying entanglement mechanism. Here, we propose several necessary and sufficient conditions for the positive-partial-transposition separability of multimode nonlinear quantum states resulting from high-order Hamiltonians and successive beam splitting operations. When applied to the initial state, the beam-splitter operations induce the emergence of different types of entanglement mechanisms, including pairwise high-order entanglement, collective high-order entanglement and the crossover between the two. We show numerically that for the four-mode scenario, the threshold for the existence of entanglement for any bipartition does not exceed the entanglement of the original state at fixed high-order moments. These results provide a new perspective for understanding multipartite nonlinear entanglement and will promote their application in quantum information processing., Comment: Comments are welcome

Exploring new unconventional superconductors is of great value for both fundamental research and practical applications. It is a long-term challenge to develop and study more hole-doped superconductors in 1111 system of iron-based superconductors. However, fifteen years after the discovery of iron-based superconductors, it has become increasingly difficult to discover new members in this system by conventional means. Here we report the discovery of superconductivity with the critical transition temperature up to 30.7 K in the parent compound CaFeAsF by an annealing treatment in air atmosphere. The superconducting behaviors are verified in both the single-crystalline and polycrystalline samples by the resistance and magnetization measurements. The analysis by combining the depth-resolved time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS) measurements show that the introduction of oxygen elements and the consequent changing in Fe valence by the annealing treatment may lead to the hole-type doping, which is the origin for the occurrence of superconductivity. Our results provide a new route to induce hole-doped superconductivity in Fe-based superconductors., Comment: 20 pages, 4 figures and 1 table

In kernel-centric operations, the uprobe component of eBPF frequently encounters performance bottlenecks, largely attributed to the overheads borne by context switches. Transitioning eBPF operations to user space bypasses these hindrances, thereby optimizing performance. This also enhances configurability and obviates the necessity for root access or privileges for kernel eBPF, subsequently minimizing the kernel attack surface. This paper introduces bpftime, a novel user-space eBPF runtime, which leverages binary rewriting to implement uprobe and syscall hook capabilities. Through bpftime, userspace uprobes achieve a 10x speed enhancement compared to their kernel counterparts without requiring dual context switches. Additionally, this runtime facilitates the programmatic hooking of syscalls within a process, both safely and efficiently. Bpftime can be seamlessly attached to any running process, limiting the need for either a restart or manual recompilation. Our implementation also extends to interprocess eBPF Maps within shared memory, catering to summary aggregation or control plane communication requirements. Compatibility with existing eBPF toolchains such as clang and libbpf is maintained, not only simplifying the development of user-space eBPF without necessitating any modifications but also supporting CO-RE through BTF. Through bpftime, we not only enhance uprobe performance but also extend the versatility and user-friendliness of eBPF runtime in user space, paving the way for more efficient and secure kernel operations.