arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 163 records · Page 9Linked to original sources

arXiv2025

Dynamical black hole entropy beyond general relativity from the Einstein frame

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.

hep-th↗

arXiv2025

Conversion of photon temporal shape using single gradient metasurface

By applying phase modulation across different frequencies, metasurfaces possess the ability to manipulate the temporal dimension of photons at the femtosecond scale. However, there remains a fundamental challenge to shape the single wavepacket at the nanosecond scale by using of metasurfaces. Here, we propose that the single photon temporal shape can be converted through the multi-photon wavepacket interference on a single metasurface. By selecting appropriate input single-photon temporal shapes and metasurfaces beam splitting ratio, controllable photon shape conversion can be achieved with high fidelity. For examples, photons with an exponentially decaying profile can be shaped into a Gaussian profile; by tuning the relative time delays of input photons, Gaussian-shaped photons can be transformed into exponentially decaying or rising profiles through the same metasurface. The proposed mechanism provides a compact way for solving the temporal shape mismatch issues in quantum networks, facilitating the realization of high-fidelity on-chip quantum information processing.

quant-ph↗

arXiv2025

Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows

Flow matching has emerged as a powerful framework for generative modeling, offering computational advantages over diffusion models by leveraging deterministic Ordinary Differential Equations (ODEs) instead of stochastic dynamics. While prior work established the worst case optimality of standard flow matching under Wasserstein distances, the theoretical guarantees for higher-order flow matching - which incorporates acceleration terms to refine sample trajectories - remain unexplored. In this paper, we bridge this gap by proving that higher-order flow matching preserves worst case optimality as a distribution estimator. We derive upper bounds on the estimation error for second-order flow matching, demonstrating that the convergence rates depend polynomially on the smoothness of the target distribution (quantified via Besov spaces) and key parameters of the ODE dynamics. Our analysis employs neural network approximations with carefully controlled depth, width, and sparsity to bound acceleration errors across both small and large time intervals, ultimately unifying these results into a general worst case optimal bound for all time steps.

cs.LG↗

arXiv2025

Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model

Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grapple with issues like model bias, limited text rendering capabilities, and insufficient understanding of Chinese cultural nuances. To address these limitations, we present Seedream 2.0, a native Chinese-English bilingual image generation foundation model that excels across diverse dimensions, which adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering. We develop a powerful data system that facilitates knowledge integration, and a caption system that balances the accuracy and richness for image description. Particularly, Seedream is integrated with a self-developed bilingual large language model as a text encoder, allowing it to learn native knowledge directly from massive data. This enable it to generate high-fidelity images with accurate cultural nuances and aesthetic expressions described in either Chinese or English. Beside, Glyph-Aligned ByT5 is applied for flexible character-level text rendering, while a Scaled ROPE generalizes well to untrained resolutions. Multi-phase post-training optimizations, including SFT and RLHF iterations, further improve the overall capability. Through extensive experimentation, we demonstrate that Seedream 2.0 achieves state-of-the-art performance across multiple aspects, including prompt-following, aesthetics, text rendering, and structural correctness. Furthermore, Seedream 2.0 has been optimized through multiple RLHF iterations to closely align its output with human preferences, as revealed by its outstanding ELO score. In addition, it can be readily adapted to an instruction-based image editing model, such as SeedEdit, with strong editing capability that balances instruction-following and image consistency.

cs.CV↗

arXiv2025

Dissipation and Decay of Three Dimensional Holographic Quantum Turbulence

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 decay behavior of the total vortex line density $L$ 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), and of $^4$He in Phys. Rev. Lett. 82, 4831 (1999) and in Phys. Rev. Lett. 118, 134501 (2017). Furthermore, 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 density, consistent with the vortex line decay equation proposed by W. F. Vinen and also the experimental measurement in Nature Physics 7, 473 - 476 (2011).

hep-th↗

arXiv2025

Enantiomer-Selective Magnetoresistance in Chiral Gold Nanocrystals by Magnetic Control of Surface Potentials

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 dozens of times, while the opposite field direction leaves it unchanged. This asymmetric response reverses for the opposite enantiomer and are reproduced in both single nanocrystals by conduction atomic force microscopy and nanocrystal thin films in solid state devices. We attribute this phenomenon to a chirality-driven charge pumping effect, where the interplay between the chiral 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.

cond-mat.mes-hall↗

arXiv2025

Holographic homogeneous superfluid on the sphere

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) on shell-shaped geometry. We investigate the superfluid phase transition from normal fluid and its Quasi-Normal Modes (QNMs) on the sphere. 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 and small black holes. 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 mode.

hep-th↗

arXiv2025

Discharge structure hierarchy of highly electronegative plasma and the implication on nuclear fusion at low pressure and quasi-cold ions approximation

In this paper, the discharge structure of an Ar/SF6 inductively coupled plasma (ICP) at the low pressure, 10 mTorr, is investigated by the fluid simulation at the quasi-cold ions approximation, i.e., room temperature. The structure is found to be hierarchal and in this simulated hierarchy, the stratification, the parabola profile in the stratified core, the double layer, and the coagulated profile in the core center are examined. This fluid simulation version and a quasi-fluid simulation of an Ar/CF4 ICP given by the HPEM code, cooperatively enlighten the discharge structure of highly electronegative ICPs and meanwhile suggest the potential applications of them. It is found that when the ions are cold the hierarchy is predicted and when the ions are thermalized the simple discharge structure appears. In the simulated hierarchy, the double layer formed at the interface of halo and core is given by the ionic and acoustic vibrations. The simulated cations flux and potential at the two sides of the double layer are double-valued and the simulated double layer is modelled as the dipole microscopically and as the capacitor macroscopically. The evolution of discharge structure hierarchy is presented, in which the harmony among many processes in the hierarchy are achieved and the charm of self-coagulation is exhibited, i.e., the bigger is the coagulated volume, the denser is the coagulated mass. This provides insights for creating possibly the free nuclear fusion by means of self-coagulation, and this type of nuclear fusion without the inertial and magnetic constrictions is achieved by means of the compressible heating scheme. The self-coagulation helps people re-recognize the anions Boltzmann balance, and meanwhile it turns the discharging and collisional plasmas into the collisionless and astrophysical plasmas, in which the double layer and ionization instability occur.

physics.plasm-ph↗

arXiv2025

Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis

Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data privacy concerns, missing data modalities are common in real-world scenarios. Existing deep learning methods tend to address it by learning an implicit latent subspace representation for different modality combinations. We identify two significant limitations of these methods: (1) implicit representation constraints that hinder the model's ability to capture modality-specific information and (2) modality heterogeneity, causing distribution gaps and redundancy in feature representations. To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations. Furthermore, we introduce a joint proxy learning module that assists IMDR in eliminating intra-modality redundancy by exploiting the extracted proxies from each class. Experiments on four ophthalmology multimodal datasets demonstrate that the proposed IMDR outperforms the state-of-the-art methods significantly.

cs.CV↗

arXiv2025

RoboGrasp: A Universal Grasping Policy for Robust Robotic Control

Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object shapes or positions. To address these limitations, we propose RoboGrasp, a universal grasping policy framework that integrates pretrained grasp detection models with robotic learning. By leveraging robust visual guidance from object detection and segmentation tasks, RoboGrasp significantly enhances grasp precision, stability, and generalizability, achieving up to 34% higher success rates in few-shot learning and grasping box prompt tasks. Built on diffusion-based methods, RoboGrasp is adaptable to various robotic learning paradigms, enabling precise and reliable manipulation across diverse and complex scenarios. This framework represents a scalable and versatile solution for tackling real-world challenges in robotic grasping.

cs.RO↗

arXiv2025

Dynamics of holographic steady flows near a first-order phase transition

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.

hep-th↗

arXiv2024

Vortex shedding patterns in holographic superfluids at finite temperature

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 $Re=\frac{v σ}ν$ (characteristic length scale $σ$, velocity $v$ and viscosity $ν$), 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.

hep-th↗

arXiv2024

AI-generated Image Quality Assessment in Visual Communication

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.

cs.CV↗

arXiv2024

Multipartite Entanglement Routing as a Hypergraph Immersion Problem

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.

quant-ph↗

arXiv2024

Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning

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.

cond-mat.dis-nn↗

arXiv2024

Spatially Visual Perception for End-to-End Robotic Learning

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.

cs.CV↗

arXiv2024

Finding hypergraph immersion is fixed-parameter tractable

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.

cs.DM↗

arXiv2024

Dynamical behaviour of soliton train in holographic superfluids at zero temperature

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.

hep-th↗