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Yue Ding

Yue Ding appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mes-hallcond-mat.mtrl-scicond-mat.supr-conMachine Learningphysics.app-phArtificial IntelligenceComputer Visionhep-th

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Published work

11 published item(s)

preprint2026arXiv

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural distortions, and semantic incoherence. While Multimodal Large Language Models (MLLMs) show strong visual understanding capabilities, their ability to perceive and reason about such artifacts remains unclear. Existing benchmarks often lack systematic evaluation of artifact-aware perception and fine-grained diagnostic reasoning, especially across diverse AI-generated video domains beyond photorealistic content. To address this gap, we introduce Artifact-Bench, a comprehensive benchmark for evaluating MLLMs on AI-generated video artifact detection and analysis. We first establish a three-level hierarchical taxonomy of realism artifacts, covering photorealistic, animated, and CG-style videos. Based on this taxonomy, Artifact-Bench defines three complementary tasks: real vs. AI-generated video classification, pairwise realism comparison, and fine-grained artifact identification. Experiments on 19 leading MLLMs reveal substantial limitations in artifact perception and reasoning, with many models approaching random or even below-random performance in challenging settings. We further observe significant misalignment between MLLM judgments and human perceptual preferences, highlighting their limited reliability as general evaluators for AI-generated video realism.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CS-GBA: A Critical Sample-based Gradient-guided Backdoor Attack for Offline Reinforcement Learning

Offline Reinforcement Learning (RL) enables policy optimization from static datasets but is inherently vulnerable to backdoor attacks. Existing attack strategies typically struggle against safety-constrained algorithms (e.g., CQL) due to inefficient random poisoning and the use of easily detectable Out-of-Distribution (OOD) triggers. In this paper, we propose CS-GBA (Critical Sample-based Gradient-guided Backdoor Attack), a novel framework designed to achieve high stealthiness and destructiveness under a strict budget. Leveraging the theoretical insight that samples with high Temporal Difference (TD) errors are pivotal for value function convergence, we introduce an adaptive Critical Sample Selection strategy that concentrates the attack budget on the most influential transitions. To evade OOD detection, we propose a Correlation-Breaking Trigger mechanism that exploits the physical mutual exclusivity of state features (e.g., 95th percentile boundaries) to remain statistically concealed. Furthermore, we replace the conventional label inversion with a Gradient-Guided Action Generation mechanism, which searches for worst-case actions within the data manifold using the victim Q-network's gradient. Empirical results on D4RL benchmarks demonstrate that our method significantly outperforms state-of-the-art baselines, achieving high attack success rates against representative safety-constrained algorithms with a minimal 5% poisoning budget, while maintaining the agent's performance in clean environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Information (MI) between different augmented views of the same graph (positive pairs). However, the GCL is limited by dimensional collapse, i.e., embedding vectors only occupy a low-dimensional subspace. In this paper, we show that the smoothing effect of the graph pooling and the implicit regularization of the graph convolution are two causes of the dimensional collapse in GCL. To mitigate the above issue, we propose a non-maximum removal graph contrastive learning approach (nmrGCL), which removes "prominent'' dimensions (i.e., contribute most in similarity measurement) for positive pair in the pre-text task. Comprehensive experiments on various benchmark datasets are conducted to demonstrate the effectiveness of nmrGCL, and the results show that our model outperforms the state-of-the-art methods. Source code will be made publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Holographic Schwinger effect in a soft wall AdS/QCD model

We perform the potential analysis for the holographic Schwinger effect in a deformed $AdS_5$ model with conformal invariance broken by a background dilaton. We evaluate the static potential by analyzing the classical action of a string attaching the rectangular Wilson loop on a probe D3 brane sitting at an intermediate position in the bulk AdS space. We observe that the inclusion of chemical potential tends to enhance the production rate, reverse to the effect of confining scale. Also, we calculate the critical electric field by Dirac-Born-Infeld (DBI) action.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The three-dimensional statistical characterization of plain grinding surfaces

In tribology, it is of importance to properly characterize the topography of rough surfaces. In this work, the three-dimensional topographies of plain grinding surfaces are measured through a white light interferometer, and their geometrical statistical features are analyzed. It is noticed that only when the total measured area is larger than a threshold value, is the statistical characterization reasonable and stable, which should be kept in mind in actual measurements. For various plain grinding surfaces, the height of asperity-summit obeys a Gaussian distribution, and the equivalent curvature radius follows a modified F-distribution. These statistical characteristics are helpful to analyze the contact and friction behaviors of rough surfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Size dependent yield hardness induced by surface energy

Size dependent hardness has long been reported in nanosized indentations, however the corresponding explanation is still in exploration. In this paper, we examine the influence of surface energy on the hardness of materials under spherical indentation. To evaluate the ability of materials to resist indentation, a yield hardness is defined here as the contact pressure at the inception of material yield. It is found that this defined hardness is an intrinsic material property depending only on the yield strength and Poisson ratio in conventional continuum mechanics. Then, the impact of surface energy on the yield hardness is analyzed through finite element simulations. By using the dimensional analysis, the dependences of the yield hardness and critical indent depth at yield initiation on surface energy have been achieved. When the yield strength is comparable to the ratio of surface energy density to indenter radius, surface energy will alter the yield hardness and the critical indent depth. As the size of indenter decreases to nanoscale, both the yield hardness and the indent depth will increase significantly. This study provides a possible clarification to the size dependence of hardness and a potential approach to measure the yield strength and surface energy of solids through nanosized indentations.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Anomalous Cooper pair interference on Bi2Te3 surface

It is believed that the edges of a chiral p-wave superconductor host Majorana modes, relating to a mysterious type of fermions predicted seven decades ago. Much attention has been paid to search for p-wave superconductivity in solid-state systems, including recently those with strong spin-orbit coupling (SOC). However, smoking-gun experiments are still awaited. In this work, we have performed phase-sensitive measurements on particularly designed superconducting quantum interference devices constructing on the surface of topological insulators Bi2Te3, in such a way that a substantial portion of the interference loop is built on the proximity-effect-induced superconducting surface. Two types of Cooper interference patterns have been recognized at low temperatures. One is s-wave like and is contributed by a zero-phase loop inhabited in the bulk of Bi2Te3. The other, being identified to relate to the surface states, is anomalous for that there is a phase shift between the positive and negative bias current directions. The results support that the Cooper pairs on the surface of Bi2Te3 have a 2πBerry phase which makes the superconductivity p_x+ip_y-wave-like. Mesoscopic hybrid rings as constructed in this experiment are presumably arbitrary-phase loops good for studying topological quantum phenomena.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Proximity effect at superconducting Sn-Bi2Se3 interface

We have investigated the conductance spectra of Sn-Bi2Se3 interface junctions down to 250 mK and in different magnetic fields. A number of conductance anomalies were observed below the superconducting transition temperature of Sn, including a small gap different from that of Sn, and a zero-bias conductance peak growing up at lower temperatures. We discussed the possible origins of the smaller gap and the zero-bias conductance peak. These phenomena support that a proximity-effect-induced chiral superconducting phase is formed at the interface between the superconducting Sn and the strong spin-orbit coupling material Bi2Se3.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Proximity-effect-induced superconductivity in topological insulator-related material Bi2Se3

We have studied the electron transport properties of topological insulator-related material Bi2Se3 near the superconducting Pb-Bi2Se3 interface, and found that a superconducting state is induced over an extended volume in Bi2Se3. This state can carry a Josephson supercurrent, and demonstrates a gap-like structure in the conductance spectra as probed by a normal-metal electrode. The establishment of the gap is not by confining the electrons into a narrow space close to the superconductor-normal metal interface, as previously observed in other systems, but presumably via electron-electron attractive interaction in Bi2Se3.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Aharonov-Casher effect in Bi$_{\rm 2}$Se$_{\rm 3}$ square-ring interferometers

Electrical control of spin dynamics in Bi$_{\rm 2}$Se$_{\rm 3}$ was investigated in ring-type interferometers. Aharonov-Bohm and Altshuler-Aronov-Spivak resistance oscillations against magnetic field, and Aharorov-Casher resistance oscillations against gate voltage were observed in the presence of a Berry phase of $π$. A very large tunability of spin precession angle by gate voltage has been obtained, indicating that Bi$_{\rm 2}$Se$_{\rm 3}$-related materials with strong spin-orbit coupling are promising candidates for constructing novel spintronic devices.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Strong Superconducting Proximity Effect in Pb-Bi2Te3 Hybrid Structures

Majarona fermions (MFs) were predicted more than seven decades ago but are yet to be identified [1]. Recently, much attention has been paid to search for MFs in condensed matter systems [2-10]. One of the seaching schemes is to create MF at the interface between an s-wave superconductor (SC) and a 3D topological insulator (TI) [11-13]. Experimentally, progresses have been achieved in the observations of a proximity-effect-induced supercurrent [14-16], a perfect Andreev reflection [17] and a conductance peak at the Fermi level [18]. However, further characterizations are still needed to clarify the nature of the SC-TI interface. In this Letter, we report on a strong proximity effect in Pb-Bi2Te3 hybrid structures, based on which Josephson junctions and superconducting quantum interference devices (SQUIDs) can be constructed. Josephson devices of this type would provide a test-bed for exploring novel phenomena such as MFs in the future.

0 citations0 reviewsNo code linkedOpen access