arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 163 records · Page 9Linked to original sources

arXiv2025

Bayesian constraints on covariant density functional equations of state of compact stars with new NICER mass-radius measurements

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

hep-ph↗

arXiv2025

Discharge structure theory of highly electronegative plasma and its hierarchy and interdisciplinary meanings

In this work the systematic theory of discharge structure is built for highly electronegative plasma, by means of self-consistent fluid model simulation that is based on the finite element method. The highly electronegative plasma is selected to be the inductively coupled Ar/SF6 plasma source with 10% the reactive SF6 concentration and in a pressure range of 10~90mTorr. The discharge structure is classified the transport dominated regime, transport and chemistry self-balanced regime and chemistry dominated regime. At low pressure of 10mTorr, the parabola feature of core plasma, stratification of whole discharge area into electronegative core and electropositive halo, anion potential barrel, and the dipole and capacitor models of double layer characterize the discharge structure of transport dominated regime. At increasing the pressure, the recombination loss of ions becomes significant and the discharge structure is characterized by ellipse profile. Meanwhile, the regions of double layer and electropositive halo are strikingly shrunk, which means that the plasma of transport and chemistry self-balanced regime is a close system and probably do not need the shield of chamber anymore. The dimensional analysis shows the recombination can be transformed into drift flux, which balances the ambi-polar diffusion of plasma species. In the range of pressure considered, simulation shows astro-structures are inlayed in the parabolic and elliptic profiles. At observing the characteristics of the astro-structures, the self-coagulation theory and quasi-Helmholtz equation are built based on the free diffusion and negative chemical source. This is the chemistry dominated regime and defined as a tight type of self-balance since the inertia is lost automatically in the unsteady state continuity equations of anions after counteracting the diffusion and recombination.

physics.plasm-ph↗

arXiv2025

Feature-Aware Malicious Output Detection and Mitigation

The rapid advancement of large language models (LLMs) has brought significant benefits to various domains while introducing substantial risks. Despite being fine-tuned through reinforcement learning, LLMs lack the capability to discern malicious content, limiting their defense against jailbreak. To address these safety concerns, we propose a feature-aware method for harmful response rejection (FMM), which detects the presence of malicious features within the model's feature space and adaptively adjusts the model's rejection mechanism. By employing a simple discriminator, we detect potential malicious traits during the decoding phase. Upon detecting features indicative of toxic tokens, FMM regenerates the current token. By employing activation patching, an additional rejection vector is incorporated during the subsequent token generation, steering the model towards a refusal response. Experimental results demonstrate the effectiveness of our approach across multiple language models and diverse attack techniques, while crucially maintaining the models' standard generation capabilities.

cs.CL↗

arXiv2025

FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation

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

cs.CV↗

arXiv2025

Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources

The reproduction of state-of-the-art multimodal LLM pre-training faces barriers at every stage of the pipeline, including high-quality data filtering, multimodal data mixture strategies, sequence packing techniques, and training frameworks. We introduce Open-Qwen2VL, a fully open-source 2B-parameter Multimodal Large Language Model pre-trained efficiently on 29M image-text pairs using only 220 A100-40G GPU hours. Our approach employs low-to-high dynamic image resolution and multimodal sequence packing to significantly enhance pre-training efficiency. The training dataset was carefully curated using both MLLM-based filtering techniques (e.g., MLM-Filter) and conventional CLIP-based filtering methods, substantially improving data quality and training efficiency. The Open-Qwen2VL pre-training is conducted on academic level 8xA100-40G GPUs at UCSB on 5B packed multimodal tokens, which is 0.36% of 1.4T multimodal pre-training tokens of Qwen2-VL. The final instruction-tuned Open-Qwen2VL outperforms partially-open state-of-the-art MLLM Qwen2-VL-2B on various multimodal benchmarks of MMBench, SEEDBench, MMstar, and MathVista, indicating the remarkable training efficiency of Open-Qwen2VL. We open-source all aspects of our work, including compute-efficient and data-efficient training details, data filtering methods, sequence packing scripts, pre-training data in WebDataset format, FSDP-based training codebase, and both base and instruction-tuned model checkpoints. We redefine "fully open" for multimodal LLMs as the complete release of: 1) the training codebase, 2) detailed data filtering techniques, and 3) all pre-training and supervised fine-tuning data used to develop the model.

cs.CL↗

arXiv2025

Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding

The key-value (KV) cache in the tensor version of transformers presents a significant bottleneck during inference. While previous work analyzes the fundamental space complexity barriers in standard attention mechanisms [Haris and Onak, 2025], our work generalizes the space complexity barriers result to tensor attention version. Our theoretical contributions rely on a reduction from communication complexity and deduce the memory lower bound for tensor-structured attention mechanisms when $d = Ω(\log n)$. Furthermore, we introduce two types of tensor attention cache and present a trade-off between time and memory for two scenarios. Overall, our work provides a theoretical foundation for us to understand the time-memory tradeoff of KV-Cache compression in tensor attention decoding and offers more perspectives in developing more memory-efficient tensor attention Transformer architectures.

cs.LG↗

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↗