arXiv subjects

Feng Ding

Publications and source records attributed to Feng Ding.

At least 37 records · Page 2Linked to original sources

arXiv2025

Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions

Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems.

cond-mat.mes-hall↗

arXiv2025

From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning

The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods, the exploration of the continuous evolution of clusters towards nanocrystal is still a big challenge. Here, we proposed a machine learning force field (MLFF) that can generalize well to various copper systems ranging from small clusters to large clusters and bulk. The continuous evolution of copper clusters CuN towards nanocrystal was revealed by investigating clusters in a wide size range (7 <= N <= 17885) based on MLFF simulated annealing. For small CuN (N < 40), electron counting rule plays a major role in stability. For large CuN (N > 80), geometric magic number rule plays a dominant role and the evolution of clusters is based on the formation of more and more icosahedral shells. For medium size CuN (40 <= N <= 80), both rules contribute. The critical size from cluster to nanocrystal was calculated to be around 8000 atoms (about 6 nm in diameter). Our work terminates the long-term challenge in nanoscience, and lay the methodological foundation for subsequent research on other cluster systems.

cond-mat.mtrl-sci↗

arXiv2025

Artificial Intelligence-Enabled Holistic Design of Catalysts Tailored for Semiconducting Carbon Nanotube Growth

Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube synthesis contains complicated nanoscale catalytic reactions, thus achieving high-density, high-quality semiconducting CNTs demands innovative catalyst design. In this work, we present a holistic framework integrating machine learning into traditional catalyst design for semiconducting CNT synthesis. It combines knowledge-based insights with data-driven techniques. Three key components, including open-access electronic structure databases for precise physicochemical descriptors, pre-trained natural language processing-based embedding model for higher-level abstractions, and physical - driven predictive models based on experiment data, are utilized. Through this framework, a new method for selective semiconducting CNT synthesis via catalyst - mediated electron injection, tuned by light during growth, is proposed. 54 candidate catalysts are screened, and three with high potential are identified. High-throughput experiments validate the predictions, with semiconducting selectivity exceeding 91% and the FeTiO3 catalyst reaching 98.6%. This approach not only addresses semiconducting CNT synthesis but also offers a generalizable methodology for global catalyst design and nanomaterials synthesis, advancing materials science in precise control.

cond-mat.mtrl-sci↗

arXiv2025

Atomically-precise synthesis and simultaneous integration of 2D transition metal dichalcogenides enabled by nano-confinement

Two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides (TMDs), and hBN, exhibit intriguing properties that are sensitive to their atomic-scale structures and can be further enriched through van der Waals (vdW) integration. However, the precise synthesis and clean integration of 2D materials remain challenging. Here, using graphene or hBN as a vdW capping layer, we create a nano-confined environment that directs the growth kinetics of 2D TMDs (e.g., NbSe2 and MoS2), enabling precise formation of TMD monolayers with tailored morphologies, from isolated monolayer domains to large-scale continuous films and intrinsically-patterned rings. Moreover, Janus S-Mo-Se monolayers are synthesized with atomic precision via vdW-protected bottom-plane chalcogen substitution. Importantly, our approach simultaneously produces ultraclean vdW interfaces. This in situ encapsulation reliably preserves air-sensitive materials, as evidenced by the enhanced superconductivity of nano-confined NbSe2 monolayers. Altogether, our study establishes a versatile platform for the controlled synthesis and integration of 2D TMDs for advanced applications.

cond-mat.mtrl-sci↗

arXiv2025

Bistability, Oscillations, and Multistability on Hycean Planets

Hycean planets are hypothetical exoplanets characterized by $H_2O$ oceans and $H_2$-rich atmospheres. These planets are high-priority targets for biosignature searches, as they combine abundant surface liquid water with easy-to-characterize $H_2$-rich atmospheres. Perhaps their most unusual climate feature is convective inhibition, which can dramatically alter a planet's temperature structure. However, so far hycean planets have mostly been investigated using 1D models that do not account for convective inhibition, and its effects are still poorly understood. This work develops pen-and-paper theory to analyze the effects of moist convective inhibition on hycean planets. The theory is tested and verified against a 1D radiative-convective model. We show that hycean planets near the onset of convective inhibition can exhibit either bistability or oscillations, due to the inhibition layer's trapping of heat and moisture. Meanwhile, hot hycean planets exhibit multistability, in which the inhibition layer and surface climate show multiple stable equilibria due to the lack of constraints on the water cycle inside the inhibition layer. The water cycle inside the inhibition layer is influenced by numerous processes that are challenging to resolve in 1D, including turbulent diffusion, convective overshoot and large-scale circulations. Our results demonstrate that hycean planets have unexpectedly rich climate dynamics. Meanwhile, previous claims about hycean planets should be treated with caution until confirmed with more self-consistent 1D and 3D models; this includes the claim that K2-18b might be habitable, and the proposal to infer $H_2O$ oceans on sub-Neptunes from JWST measurements of chemical species in their upper atmospheres.

astro-ph.EP↗

arXiv2025

Brought a Gun to a Knife Fight: Modern VFM Baselines Outgun Specialized Detectors on In-the-Wild AI Image Detection

While specialized detectors for AI-generated images excel on curated benchmarks, they fail catastrophically in real-world scenarios, as evidenced by their critically high false-negative rates on `in-the-wild' benchmarks. Instead of crafting another specialized `knife' for this problem, we bring a `gun' to the fight: a simple linear classifier on a modern Vision Foundation Model (VFM). Trained on identical data, this baseline decisively `outguns' bespoke detectors, boosting in-the-wild accuracy by a striking margin of over 20\%. Our analysis pinpoints the source of the VFM's `firepower': First, by probing text-image similarities, we find that recent VLMs (e.g., Perception Encoder, Meta CLIP2) have learned to align synthetic images with forgery-related concepts (e.g., `AI-generated'), unlike previous versions. Second, we speculate that this is due to data exposure, as both this alignment and overall accuracy plummet on a novel dataset scraped after the VFM's pre-training cut-off date, ensuring it was unseen during pre-training. Our findings yield two critical conclusions: 1) For the real-world `gunfight' of AI-generated image detection, the raw `firepower' of an updated VFM is far more effective than the `craftsmanship' of a static detector. 2) True generalization evaluation requires test data to be independent of the model's entire training history, including pre-training.

cs.CV↗

arXiv2025

Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection

Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often suffer from a dual-overfitting: they become overly specialized in both specific forgery fingerprints and particular demographic attributes. Critically, most existing methods overlook the latter issue, which results in poor fairness: faces from certain demographic groups, such as different genders or ethnicities, are consequently more difficult to reliably detect. To address this challenge, we propose a novel strategy called misleading-learning, which populates the latent space with a multitude of redundant environments. By exposing the detector to a sufficiently rich and balanced variety of high-level information for demographic fairness, our approach mitigates demographic bias while maintaining a high detection performance level. We conduct extensive evaluations on fairness, intra-domain detection, cross-domain generalization, and robustness. Experimental results demonstrate that our framework achieves superior fairness and generalization compared to state-of-the-art approaches.

cs.CV↗

arXiv2025

LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition

Skeleton-based action recognition faces two longstanding challenges: the scarcity of labeled training samples and difficulty modeling short- and long-range temporal dependencies. To address these issues, we propose a unified framework, LSTC-MDA, which simultaneously improves temporal modeling and data diversity. We introduce a novel Long-Short Term Temporal Convolution (LSTC) module with parallel short- and long-term branches, these two feature branches are then aligned and fused adaptively using learned similarity weights to preserve critical long-range cues lost by conventional stride-2 temporal convolutions. We also extend Joint Mixing Data Augmentation (JMDA) with an Additive Mixup at the input level, diversifying training samples and restricting mixup operations to the same camera view to avoid distribution shifts. Ablation studies confirm each component contributes. LSTC-MDA achieves state-of-the-art results: 94.1% and 97.5% on NTU 60 (X-Sub and X-View), 90.4% and 92.0% on NTU 120 (X-Sub and X-Set),97.2% on NW-UCLA. Code: https://github.com/xiaobaoxia/LSTC-MDA.

cs.CV↗

arXiv2025

Detecting Multimedia Generated by Large AI Models: A Survey

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, online detection tools, and evaluation metrics to provide a valuable resource for researchers and practitioners in this field. Most importantly, we offer a focused analysis from a social media perspective to highlight their broader societal impact. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey.

cs.MM↗

arXiv2025

Step-directed Epitaxy of Uni-directional Hexagonal Boron Nitride on Vicinal Ge(110)

Insulating hexagonal boron nitride (hBN) films with precisely controlled thickness are ideal dielectric components to modulate various interfaces in electronic devices. To achieve this, high-quality hBN with controlled atomic configurations must be able to form pristine interfaces with various materials in devices. However, previously reported large-scale hBN films with uniform thickness either are polycrystalline or are not suitable for atomically clean assembly via mechanical exfoliation, limiting their applications in device technology. Here, we report the large-scale growth of monolayer single crystalline hBN films on Ge(110) substrates by using chemical vapor deposition (CVD). Vicinal Ge(110) substrates are used for the step-directed epitaxial growth of hBN, where Ge atomic steps act as the hBN nucleation sites, guiding the uni-directional alignments of multiple hBN domains. Density functional theory (DFT) calculations reveal that the optimum hydrogen passivations on both hBN edges and Ge surfaces enable the epitaxial coupling between hBN and the Ge step edges and the single crystallinity of the final hBN films. Using epitaxially grown monolayer hBN films, we fabricate a few hBN films with controlled stacking orders and pristine interfaces through a layer-by-layer assembly process. These films function as high-quality dielectrics to enhance carrier transport in graphene and MoS 2 channels.

cond-mat.mtrl-sci↗

arXiv2025

Customizable ROI-Based Deep Image Compression

Region of Interest (ROI)-based image compression optimizes bit allocation by prioritizing ROI for higher-quality reconstruction. However, as the users (including human clients and downstream machine tasks) become more diverse, ROI-based image compression needs to be customizable to support various preferences. For example, different users may define distinct ROI or require different quality trade-offs between ROI and non-ROI. Existing ROI-based image compression schemes predefine the ROI, making it unchangeable, and lack effective mechanisms to balance reconstruction quality between ROI and non-ROI. This work proposes a paradigm for customizable ROI-based deep image compression. First, we develop a Text-controlled Mask Acquisition (TMA) module, which allows users to easily customize their ROI for compression by just inputting the corresponding semantic \emph{text}. It makes the encoder controlled by text. Second, we design a Customizable Value Assign (CVA) mechanism, which masks the non-ROI with a changeable extent decided by users instead of a constant one to manage the reconstruction quality trade-off between ROI and non-ROI. Finally, we present a Latent Mask Attention (LMA) module, where the latent spatial prior of the mask and the latent Rate-Distortion Optimization (RDO) prior of the image are extracted and fused in the latent space, and further used to optimize the latent representation of the source image. Experimental results demonstrate that our proposed customizable ROI-based deep image compression paradigm effectively addresses the needs of customization for ROI definition and mask acquisition as well as the reconstruction quality trade-off management between the ROI and non-ROI.

cs.CV↗

arXiv2025

How oxygen influences the catalytic activity of iron during carbon nanotube nucleation

The catalytic activity of metal nanoparticles toward nucleation of single-walled carbon nanotubes (SWCNTs) is fundamental to achieving structure-controlled growth using catalytic chemical vapor deposition (CVD). Despite the success of oxidized catalysts in SWCNT growth, there is a lack of understanding regarding how oxygen influences the catalysts and the nucleation process. Quantum chemical molecular dynamics (MD) simulations employing density functional tight binding (DFTB) demonstrate that the kinetics of carbon nucleation on an iron nanoparticle catalyst can be tuned via oxygen loading. Increasing the oxygen content in the catalyst leads to activation of surface-bound carbon species and enhanced carbon chain growth due to respective weakening and strengthening of the C-C and Fe-C bonding. This is due to oxygen modulating the electronic structure of the iron catalyst, with the Fermi level of the catalyst increasing proportionally with oxygen content until the iron:oxygen stoichiometry reaches parity. The increase in Fe 3d states near the Fermi level also promotes the donation of electron density into unoccupied C 2p states, activating C-C bonds which in turn facilitates carbon chain growth and slows carbon ring condensation.

cond-mat.mtrl-sci↗

arXiv2025

Retention of surface water on tidally locked rocky planets in the Venus zone around M dwarfs

Terrestrial planets within the Venus zone surrounding M dwarf stars can retain surface ice caps on the perpetual dark side if atmospheric heat transport is inefficient, {as suggested by previous global climate simulations \citep[e.g.,][]{leconte2013}.} This condition is {proposed} to play a role in the potential regional habitability of these planets. However, the amount of surface ice may be limited by considering the water condensed from the steam atmosphere in a runaway greenhouse state, and the physical mechanism for triggering the condensation process is not clear. Here, we use a two-column moist radiative-convective-subsiding model to investigate the water condensation process on tidally locked planets from the runaway greenhouse state. We find that the water condensation process is characterized by two distinct equilibrium states under the same {incoming stellar flux}. The initiation of condensation corresponds to a warm, unstable state exhibiting positive Planck feedback, whereas the termination phase corresponds to a cold, stable state exhibiting negative Planck feedback. We further show that the surface water mass in the collapsed state {decreases with the} incoming stellar flux, background surface pressure, and optical thickness of non-condensible greenhouse gases, with a global equivalent depth of less than $\sim 20$ cm. Our two-column approach provides a straightforward way to understand the water evolution on Venus zone planets around M dwarfs.

astro-ph.EP↗

arXiv2025

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators. We trace this failure in part to latent prior bias: detectors learn shortcuts tied to patterns stemming from the initial noise vector rather than learning robust generative artifacts. To address this, we propose On-Manifold Adversarial Training (OMAT): by optimizing the initial latent noise of diffusion models under fixed conditioning, we generate on-manifold adversarial examples that remain on the generator's output manifold-unlike pixel-space attacks, which introduce off-manifold perturbations that the generator itself cannot reproduce and that can obscure the true discriminative artifacts. To test against state-of-the-art generative models, we introduce GenImage++, a test-only benchmark of outputs from advanced generators (Flux.1, SD3) with extended prompts and diverse styles. We apply our adversarial-training paradigm to ResNet50 and CLIP baselines and evaluate across existing AIGC forensic benchmarks and recent challenge datasets. Extensive experiments show that adversarially trained detectors significantly improve cross-generator performance without any network redesign. Our findings on latent-prior bias offer valuable insights for future dataset construction and detector evaluation, guiding the development of more robust and generalizable AIGC forensic methodologies.

cs.CV↗

arXiv2025

Influence of Planetary Rotation on Supersonic Flow of Lava Planets: A Two-Dimensional Horizontal Model Analysis

The study of lava planets has attracted significant attention recently because of their close proximity to their host stars, which enhances their detectability for atmospheric characterization. Previous studies showed that the atmospheric flow becomes supersonic if the atmosphere was dominated by rocky vapor evaporated from the magma ocean around the substellar point of small lava planets. These studies often assumed an axisymmetric flow about the axis from the substellar point to the antistellar point but ignored the effect of planetary rotation on the climate. The spin rate of lava planets can be rather fast due to their close-in orbits, which can break the aforementioned symmetry and induce the asymmetric flow component. Here, we introduce a two-dimensional framework to explore the influence of planetary rotation on the atmospheric dynamics of these lava planets for the first time, and assess the sensitivity and range of application of our model. Starting from the established one-dimensional axisymmetric atmospheric solution, we obtain the governing equation for the asymmetric flow by expanding with respect to 1/Ro (Ro denotes Rossby number and exceeds unity for typical lava planets). The asymmetric component of supersonic flow is pivotal for future research on the observation of these atmospheres, flow patterns of the magma ocean currents driven by atmospheric winds, and deformation of the planetary shape over long timescales.

astro-ph.EP↗

arXiv2025

HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs

Outdated facts in temporal knowledge graphs (TKGs) result from exceeding the expiration date of facts, which negatively impact reasoning performance on TKGs. However, existing reasoning methods primarily focus on positive importance of historical facts, neglecting adverse effects of outdated facts. Besides, training on these outdated facts yields extra computational cost. To address these challenges, we propose an outdated fact filtering framework named HALO, which quantifies the temporal validity of historical facts by exploring the half-life theory to filter outdated facts in TKGs. HALO consists of three modules: the temporal fact attention module, the dynamic relation-aware encoder module, and the outdated fact filtering module. Firstly, the temporal fact attention module captures the evolution of historical facts over time to identify relevant facts. Secondly, the dynamic relation-aware encoder module is designed for efficiently predicting the half life of each fact. Finally, we construct a time decay function based on the half-life theory to quantify the temporal validity of facts and filter outdated facts. Experimental results show that HALO outperforms the state-of-the-art TKG reasoning methods on three public datasets, demonstrating its effectiveness in detecting and filtering outdated facts (Codes are available at https://github.com/yushuowiki/K-Half/tree/main ).

cs.AI↗

arXiv2025

Formation of Near-surface Atmospheric Inversion and Surface Inversion in Hothouse Climates

A hothouse climate may develop throughout Earth's history and its warming future and on potentially habitable exoplanets near the inner edge of the habitable zone. Previous studies suggested that near-surface atmospheric inversion (NAIV) with planetary boundary air temperature being higher than the air temperature adjacent to the surface, is a pronounced phenomenon in hothouse climates. However, the underlying mechanisms are unclear. Here we show that lower-tropospheric radiative heating is necessary but not independently sufficient in forming the NAIV. Instead, the dynamic heating induced by large-scale subsidence is essential. With the prescribed reasonable large-scale subsidence, NAIV appears in small-domain cloud-resolving simulations, which was not observed in previous studies. Surface evaporative cooling also contributes to the formation of the NAIV. Besides NAIV, we find that surface inversion (SIV) with the air adjacent to the surface being warmer than the underlying sea surface is also a distinct phenomenon in hothouse climates. SIV is caused by strong surface evaporative cooling and large atmospheric shortwave absorption. These two types of inversion strongly stabilize the atmosphere, weaken atmospheric circulation, dry the free troposphere, and suppress the hydrological cycle.

astro-ph.EP↗

arXiv2025

VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models

Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only yield a binary decision, incapable of forgery localization, attribution of forgery methods, and providing analysis on the cause of forgeries. In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics, and propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators. To achieve the above goals, we introduce VLF (Visual Language Forensics), a novel and diverse synthesis face dataset designed to facilitate rich interactions between Visual and Language modalities in MLLMs. Additionally, we propose an extrinsic knowledge-guided description method, termed EkCot, which leverages knowledge from the image generation pipeline to enable MLLMs to quickly capture image content. Furthermore, we introduce a low-level vision comparison pipeline designed to identify differential features between real and fake that MLLMs can inherently understand. These features are then incorporated into EkCot, enhancing its ability to analyze forgeries in a structured manner, following the sequence of detection, localization, and attribution. Extensive experiments demonstrate that VLForgery outperforms other state-of-the-art forensic approaches in detection accuracy, with additional potential for falsified region localization and attribution analysis.

cs.CV↗