arXiv subjects

Feng Xiao

Publications and source records attributed to Feng Xiao.

At least 55 records · Page 3Linked to original sources

arXiv2024

A low-dissipation numerical method based on boundary variation diminishing principle for compressible gas-liquid two-phase flows with phase change on unstructured grid

A low-dissipation numerical method for compressible gas-liquid two-phase flow with phase change on unstructured grids is proposed. The governing equations adopt the six-equation model. The non-conservative terms included in the volume fraction and total energy equations of the six-equation model are defined on cell boundaries using second-order accurate approximations and calculated without interpolating the spatial derivatives. To capture discontinuities such as contact discontinuities and gas-liquid interfaces with low dissipation, the MUSCL-THINC/QQ-BVD scheme, which combines the Monotone Upstream-centered Schemes for Conservation Laws (MUSCL) method and the Tangent Hyperbola for INterface Capturing method with Quadratic surface representation and Gaussian Quadrature (THINC/QQ) method, is employed. The MUSCL method is one of the mainstream numerical solvers for compressible flows, achieving second-order accuracy for smooth solutions, but it introduces excessive numerical dissipation errors near discontinuous solutions. The THINC/QQ method uses a reconstruction function developed for interface capturing on unstructured grids, making use of a sigmoidal function with a quadratic surface. By combining these reconstruction functions according to the Boundary Variation Diminishing (BVD) principle, the MUSCL method is selected for smooth solutions, while the THINC/QQ method is chosen for discontinuous solutions, preserving the solution structure accurately. Several benchmark tests are solved, demonstrating that the MUSCL-THINC/QQ-BVD scheme not only captures contact discontinuities with low dissipation but also resolves dynamically generated gas-liquid interfaces due to phase changes clearly.

physics.flu-dyn↗

arXiv2024

4DStyleGaussian: Zero-shot 4D Style Transfer with Gaussian Splatting

3D neural style transfer has gained significant attention for its potential to provide user-friendly stylization with spatial consistency. However, existing 3D style transfer methods often fall short in terms of inference efficiency, generalization ability, and struggle to handle dynamic scenes with temporal consistency. In this paper, we introduce 4DStyleGaussian, a novel 4D style transfer framework designed to achieve real-time stylization of arbitrary style references while maintaining reasonable content affinity, multi-view consistency, and temporal coherence. Our approach leverages an embedded 4D Gaussian Splatting technique, which is trained using a reversible neural network for reducing content loss in the feature distillation process. Utilizing the 4D embedded Gaussians, we predict a 4D style transformation matrix that facilitates spatially and temporally consistent style transfer with Gaussian Splatting. Experiments demonstrate that our method can achieve high-quality and zero-shot stylization for 4D scenarios with enhanced efficiency and spatial-temporal consistency.

cs.CV↗

arXiv2024

ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model

Despite recent advancements in 3D generation methods, achieving controllability still remains a challenging issue. Current approaches utilizing score-distillation sampling are hindered by laborious procedures that consume a significant amount of time. Furthermore, the process of first generating 2D representations and then mapping them to 3D lacks internal alignment between the two forms of representation. To address these challenges, we introduce ControLRM, an end-to-end feed-forward model designed for rapid and controllable 3D generation using a large reconstruction model (LRM). ControLRM comprises a 2D condition generator, a condition encoding transformer, and a triplane decoder transformer. Instead of training our model from scratch, we advocate for a joint training framework. In the condition training branch, we lock the triplane decoder and reuses the deep and robust encoding layers pretrained with millions of 3D data in LRM. In the image training branch, we unlock the triplane decoder to establish an implicit alignment between the 2D and 3D representations. To ensure unbiased evaluation, we curate evaluation samples from three distinct datasets (G-OBJ, GSO, ABO) rather than relying on cherry-picking manual generation. The comprehensive experiments conducted on quantitative and qualitative comparisons of 3D controllability and generation quality demonstrate the strong generalization capacity of our proposed approach.

cs.CV↗

arXiv2024

Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers

Accurately estimating data in sensor-less areas is crucial for understanding system dynamics, such as traffic state estimation and environmental monitoring. This study addresses challenges posed by sparse sensor deployment and unreliable data by framing the problem as a spatiotemporal kriging task and proposing a novel graph transformer model, Kriformer. This model estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources. Kriformer utilizes transformer architecture to enhance the model's perceptual range and solve edge information aggregation challenges, capturing spatiotemporal information effectively. A carefully constructed positional encoding module embeds the spatiotemporal features of nodes, while a sophisticated spatiotemporal attention mechanism enhances estimation accuracy. The multi-head spatial interaction attention module captures subtle spatial relationships between observed and unobserved locations. During training, a random masking strategy prompts the model to learn with partial information loss, allowing the spatiotemporal embedding and multi-head attention mechanisms to synergistically capture correlations among locations. Experimental results show that Kriformer excels in representation learning for unobserved locations, validated on two real-world traffic speed datasets, demonstrating its effectiveness in spatiotemporal kriging tasks.

cs.LG↗

arXiv2024

State Estimator Design: Addressing General Delay Structures with Dissipative Constraints

Dissipative estimator (observer) design for continuous time-delay systems poses a significant challenge when an unlimited number of pointwise and general distributed delays (DDs) are concerned. We propose an effective solution to this semi-open problem using the Krasovski\uı functional (KF) framework in conjunction with a quadratic supply rate function, where both the plant and the estimator can accommodate an unlimited number of pointwise and general distributed delays with an unlimited number of square-integrable kernels. A key contribution is the introduction of a control concept called Kronecker-Seuret Decomposition (KSD) for matrix-valued functions, which allows for the factorizations or approximations of any DD integral kernel without introducing conservatism. Moreover, using KSD facilitates the construction of complete-type KFs with integral kernels that can contain any number of weakly differentiable and linearly independent functions. Our proposed solution is formulated as sequential convex SDP problems and is set out in two theorems along with an off-line iterative algorithm, which eliminates the need for nonlinear numerical solvers. We show the effectiveness of our method using two challenging numerical experiments, including a system stabilized by a non-smooth controller.

math.OC↗

arXiv2024

RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent

Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expanded the attack surface of LLMs, exposing them to a variety of threats. Among them, jailbreak attacks that induce toxic responses through jailbreak prompts have raised critical safety concerns. To identify these threats, a growing number of red teaming approaches simulate potential adversarial scenarios by crafting jailbreak prompts to test the target LLM. However, existing red teaming methods do not consider the unique vulnerabilities of LLM in different scenarios, making it difficult to adjust the jailbreak prompts to find context-specific vulnerabilities. Meanwhile, these methods are limited to refining jailbreak templates using a few mutation operations, lacking the automation and scalability to adapt to different scenarios. To enable context-aware and efficient red teaming, we abstract and model existing attacks into a coherent concept called "jailbreak strategy" and propose a multi-agent LLM system named RedAgent that leverages these strategies to generate context-aware jailbreak prompts. By self-reflecting on contextual feedback in an additional memory buffer, RedAgent continuously learns how to leverage these strategies to achieve effective jailbreaks in specific contexts. Extensive experiments demonstrate that our system can jailbreak most black-box LLMs in just five queries, improving the efficiency of existing red teaming methods by two times. Additionally, RedAgent can jailbreak customized LLM applications more efficiently. By generating context-aware jailbreak prompts towards applications on GPTs, we discover 60 severe vulnerabilities of these real-world applications with only two queries per vulnerability. We have reported all found issues and communicated with OpenAI and Meta for bug fixes.

cs.CR↗

arXiv2024

Integral Inequalities for the Analysis of Distributed Parameter Systems: A complete characterization via the Least-Squares Principle

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted $\mathcal{L}^2$ functions that are linearly independent in a Lebesgue sense. Moreover, we not only establish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

math.OC↗

arXiv2024

Scalable second-order consensus of hierarchical groups

Motivated by widespread dominance hierarchy, growth of group sizes, and feedback mechanisms in social species, we are devoted to exploring the scalable second-order consensus of hierarchical groups. More specifically, a hierarchical group consists of a collection of agents with double-integrator dynamics on a directed acyclic graph with additional reverse edges, which characterize feedback mechanisms across hierarchical layers. As the group size grows and the reverse edges appear, we investigate whether the absolute velocity protocol and the relative velocity protocol can preserve the system consensus property without tuning the control gains. It is rigorously proved that the absolute velocity protocol is able to achieve completely scalable second-order consensus but the relative velocity protocol cannot. This result theoretically reveals how the scalable coordination behavior in hierarchical groups is determined by local interaction rules. Moreover, we develop a hierarchical structure in order to achieve scalable second-order consensus for networks of any size and with any number of reverse edges.

eess.SY↗

arXiv2024

SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention

3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description. Existing works fail to distinguish similar objects especially when multiple referred objects are involved in the description. Experiments show that direct matching of language and visual modal has limited capacity to comprehend complex referential relationships in utterances. It is mainly due to the interference caused by redundant visual information in cross-modal alignment. To strengthen relation-orientated mapping between different modalities, we propose SeCG, a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer. Our method replaces original language-independent encoding with cross-modal encoding in visual analysis. More text-related feature expressions are obtained through the guidance of global semantics and implicit relationships. Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods, particularly improving the localization performance for the multi-relation challenges.

cs.CV↗

arXiv2024

StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields

4D style transfer aims at transferring arbitrary visual style to the synthesized novel views of a dynamic 4D scene with varying viewpoints and times. Existing efforts on 3D style transfer can effectively combine the visual features of style images and neural radiance fields (NeRF) but fail to handle the 4D dynamic scenes limited by the static scene assumption. Consequently, we aim to handle the novel challenging problem of 4D style transfer for the first time, which further requires the consistency of stylized results on dynamic objects. In this paper, we introduce StyleDyRF, a method that represents the 4D feature space by deforming a canonical feature volume and learns a linear style transformation matrix on the feature volume in a data-driven fashion. To obtain the canonical feature volume, the rays at each time step are deformed with the geometric prior of a pre-trained dynamic NeRF to render the feature map under the supervision of pre-trained visual encoders. With the content and style cues in the canonical feature volume and the style image, we can learn the style transformation matrix from their covariance matrices with lightweight neural networks. The learned style transformation matrix can reflect a direct matching of feature covariance from the content volume to the given style pattern, in analogy with the optimization of the Gram matrix in traditional 2D neural style transfer. The experimental results show that our method not only renders 4D photorealistic style transfer results in a zero-shot manner but also outperforms existing methods in terms of visual quality and consistency.

cs.CV↗

arXiv2023

Distributed event-triggered aggregative optimization with applications to price-based energy management

This paper studies a distributed continuous-time aggregative optimization problem, which is a fundamental problem in the price-based energy management. The objective of the distributed aggregative optimization is to minimize the sum of local objective functions, which have a specific expression that relies on agents' own decisions and the aggregation of all agents' decisions. To solve the problem, a novel distributed continuous-time algorithm is proposed by combining gradient dynamics with a dynamic average consensus estimator in a two-time scale. The exponential convergence of the proposed algorithm is established under the assumption of a convex global cost function by virtue of the stability theory of singular perturbation systems. Motivated by practical applications, the implementation of the continuous-time algorithm with event-triggered communication is investigated. Simulations on the price-based energy management of distributed energy resources are given to illustrate the proposed method.

math.OC↗

arXiv2023

Hilbert expansion of the Boltzmann equation in the incompressible Euler level in a channel

The study of hydrodynamic limit of the Boltzmann equation with physical boundary is a challenging problem due to appearance of the viscous and Knudsen boundary layers. In this paper, the hydrodynamic limit from the Boltzmann equation with specular reflection boundary condition to the incompressible Euler in a channel is investigated. Based on the multiscaled Hilbert expansion, the equations with boundary conditions and compatibility conditions for interior solutions, viscous and Knudsen boundary layers are derived under different scaling, respectively. Then some uniform estimates for the interior solutions, viscous and Knudsen boundary layers are established. With the help of $L^2-L^\infty$ framework and the uniform estimates obtained above, the solutions to the Boltzmann equation are constructed by the truncated Hilbert expansion with multiscales, and hence the hydrodynamic limit in the incompressible Euler level is justified.

math.AP↗

arXiv2023

Restricted Generative Projection for One-Class Classification and Anomaly Detection

We present a simple framework for one-class classification and anomaly detection. The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution. Crucially, the target distribution should be sufficiently simple, compact, and informative. The simplicity is to ensure that we can sample from the distribution easily, the compactness is to ensure that the decision boundary between normal data and abnormal data is clear and reliable, and the informativeness is to ensure that the transformed data preserve the important information of the original data. Therefore, we propose to use truncated Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between hyperspheres, as the target distribution. We then minimize the distance between the transformed data distribution and the target distribution while keeping the reconstruction error for the original data small enough. Comparative studies on multiple benchmark datasets verify the effectiveness of our methods in comparison to baselines.

cs.LG↗

arXiv2023

Distributed continuous-time strategy-updating rules for noncooperative games with discrete-time communication

In this paper, continuous-time noncooperative games in networks of double-integrator agents are explored. The existing methods require that agents communicate with their neighbors in real time. In this paper, we propose two discrete-time communication schemes based on the designed continuous-time strategy-updating rule for the efficient use of communication resources. First, the property of the designed continuous-time rule is analyzed to ensure that all agents' strategies can converge to the Nash equilibrium. Then, we propose periodic and event-triggered communication schemes for the implementation of the designed rule with discrete-time communication. The rule in the periodic case is implemented synchronously and easily. The rule in the event-triggered case is executed asynchronously without Zeno behaviors. All agents in both cases can asymptotically reach to the Nash equilibrium by interacting with neighbors at discrete times. Simulations are performed in networks of Cournot competition to illustrate the effectiveness of the proposed methods.

math.OC↗

arXiv2023

Pressure-induced dimerization and molecular orbitals formation in Na2RuO3 with strong correlation-enhanced spin-orbit coupling effect

First-principles calculations and simulations are conducted to clarify the nonmagnetic insulating ground state of the honeycomb lattice compound Na2RuO3 with 4d^4 electronic configuration and explore the evolutions of crystal structure and electronic property under pressure. We reveal that individual Coulomb correlation or spin-orbit coupling (SOC) effect cannot reproduce the experimentally observed nonmagnetic insulating behavior of Na2RuO3, whereas the Coulomb correlation enhanced SOC interactions give rise to an unusual spin-orbital-entangled J = 0 nonmagnetic insulating state, which contrasts with the SOC assisted Mott insulating state in d^5 ruthenates and iridates. Furthermore, a pressure-induced structural dimerization transition has been predicted around 15-17.5 GPa. The honeycomb lattice of the high-pressure dimerized phase features with parallel pattern of the short Ru-Ru dimers aligning along the crystallographic b direction. Accompanied with the structural dimerization, the electronic structure shows striking reconstruction by formation of molecular orbitals. Interestingly, the cooperation of Coulomb correlation together with SOC can realize a nonmagnetic insulating state in the high-pressure dimerized phase. The d^4 ruthenate Na2RuO3 with honeycomb lattice will provide a new platform to explore unusual physics and rich phase diagram due to the delicate interplay of lattice degree of freedom, electron correlations, and SOC interactions.

cond-mat.str-el↗

arXiv2023

Optimal subsampling for the Cox proportional hazards model with massive survival data

The use of massive survival data has become common in survival analysis. In this study, a subsampling algorithm is proposed for the Cox proportional hazards model with time-dependent covariates when the sample is extraordinarily large but computing resources are relatively limited. A subsample estimator is developed by maximizing the weighted partial likelihood; it is shown to have consistency and asymptotic normality. By minimizing the asymptotic mean squared error of the subsample estimator, the optimal subsampling probabilities are formulated with explicit expressions. Simulation studies show that the proposed method can satisfactorily approximate the estimator of the full dataset. The proposed method is then applied to corporate loan and breast cancer datasets, with different censoring rates, and the outcomes confirm its practical advantages.

stat.CO↗

arXiv2022

Local Well-posedness of Unsteady Potential Flows Near a Space Corner of Right Angle

In this paper we are concerned with the local well-posedness of the unsteady potential flows near a space corner of right angle, which could be formulated as an initial-boundary value problem of a hyperbolic equation of second order in a cornered-space domain. The corner singularity is the key difficulty in establishing the local well-posedness of the problem. Moreover, the boundary conditions on both edges of the corner angle are of Neumann-type and fail to satisfy the linear stability condition, which makes it more difficult to establish a priori estimates on the boundary terms in the analysis. In this paper, extension methods will be updated to deal with the corner singularity, and, based on a key observation that the boundary operators are co-normal, new techniques will be developed to control the boundary terms.

math.AP↗

arXiv2022

A nonhydrostatic atmospheric dynamical core on cubed sphere using hybrid multi-moment finite-volume/finite difference methods: formulations and preliminary tests

A nonhydrostatic dynamical core has been developed by using the multi-moment finite volume method that ensures the rigorous numerical conservation. To represent the spherical geometry free of polar problems, the cubed-sphere grid is adopted. A fourth-order multi-moment discretization formulation is applied to solve the governing equations cast in the local curvilinear coordinates on each patch of cubed sphere through a gnomonic projection. In vertical direction, the height-based terrain-following grid is used to deal with the topography and a conservative finite difference scheme is adopted for the spatial discretization. The dynamical core adopts the nonhydrostatic governing equations. To get around the CFL stability restriction imposed by sound wave propagation and relatively small grid spacing in the vertical direction, the dimensional-splitting time integration algorithm using the HEVI (horizontally-explicit and vertically-implicit) strategy is implemented by applying the IMEX (implicit-explicit) Runge-Kutta method. The proposed model was checked by the widely-used benchmark tests in this study. The numerical results show that the multi-moment model has superior solution quality and great practical potential as a numerical platform for development of the atmospheric general circulation models.

physics.ao-ph↗