arXiv subjects

Qing Zhang

Publications and source records attributed to Qing Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Improving Viewpoint Consistency in 3D Generation via Structure Feature and CLIP Guidance

Despite recent advances in text-to-3D generation techniques, current methods often suffer from geometric inconsistencies, commonly referred to as the Janus Problem. This paper identifies the root cause of the Janus Problem: viewpoint generation bias in diffusion models, which creates a significant gap between the actual generated viewpoint and the expected one required for optimizing the 3D model. To address this issue, we propose a tuning-free approach called the Attention and CLIP Guidance (ACG) mechanism. ACG enhances desired viewpoints by adaptively controlling cross-attention maps, employs CLIP-based view-text similarities to filter out erroneous viewpoints, and uses a coarse-to-fine optimization strategy with staged prompts to progressively refine 3D generation. Extensive experiments demonstrate that our method significantly reduces the Janus Problem without compromising generation speed, establishing ACG as an efficient, plug-and-play component for existing text-to-3D frameworks.

cs.CV↗

arXiv2025

Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification

Accurate prediction of placental diseases via whole slide images (WSIs) is critical for preventing severe maternal and fetal complications. However, WSI analysis presents significant computational challenges due to the massive data volume. Existing WSI classification methods encounter critical limitations: (1) inadequate patch selection strategies that either compromise performance or fail to sufficiently reduce computational demands, and (2) the loss of global histological context resulting from patch-level processing approaches. To address these challenges, we propose an Efficient multimodal framework for Patient-level placental disease Diagnosis, named EmmPD. Our approach introduces a two-stage patch selection module that combines parameter-free and learnable compression strategies, optimally balancing computational efficiency with critical feature preservation. Additionally, we develop a hybrid multimodal fusion module that leverages adaptive graph learning to enhance pathological feature representation and incorporates textual medical reports to enrich global contextual understanding. Extensive experiments conducted on both a self-constructed patient-level Placental dataset and two public datasets demonstrating that our method achieves state-of-the-art diagnostic performance. The code is available at https://github.com/ECNU-MultiDimLab/EmmPD.

cs.CV↗

arXiv2025

Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking

Evaluation plays a crucial role in the development of ranking algorithms on search and recommender systems. It enables online platforms to create user-friendly features that drive commercial success in a steady and effective manner. The online environment is particularly conducive to applying causal inference techniques, such as randomized controlled experiments (known as A/B test), which are often more challenging to implement in fields like medicine and public policy. However, businesses face unique challenges when it comes to effective A/B test. Specifically, achieving sufficient statistical power for conversion-based metrics can be time-consuming, especially for significant purchases like booking accommodations. While offline evaluations are quicker and more cost-effective, they often lack accuracy and are inadequate for selecting candidates for A/B test. To address these challenges, we developed interleaving and counterfactual evaluation methods to facilitate rapid online assessments for identifying the most promising candidates for A/B tests. Our approach not only increased the sensitivity of experiments by a factor of up to 100 (depending on the approach and metrics) compared to traditional A/B testing but also streamlined the experimental process. The practical insights gained from usage in production can also benefit organizations with similar interests.

cs.IR↗

arXiv2025

Electron Doping Stabilization of Highly-Polar Supertetragonal BaSnO3

Could electrons stabilize ferroelectric polarization in unpolarized system? Basically, electron doping was thought to be contrary to polarization due to the well-known picture that the screening effect on Coulomb interaction diminishes ferroelectric polarization. However, in this paper, we propose a novel mechanism of stabilizing highly-polar supertetragonal BaSnO3 by electron doping. With moderate compressive strain applied, less than -5.5%, BaSnO3 exhibits stable nonpolarized normal tetragonal structure and an unstable supertetragonal state which is characterized with extremely large c/a ratio and giant polarization. We found that the band gap of the supertetragonal state is much smaller than the normal tetragonal state, with a difference around 1.2eV. Therefore, the energy of the doped electrons selectively favors the smaller gap supertetragonal state than the larger band gap normal tetragonal state, and the critical strain to stabilize the supertetragonal phase could be reduced by electron doping. This mechanism guarantees the controllable supertetragonal structures by electron doping and ensures the coexistence of giant polarization and conducting in high-mobility BaSnO3, and is promising to design high-mobility ferroelectrics conductor.

cond-mat.mtrl-sci↗

arXiv2025

Large magnon dichroism and other optical properties of hexagonal ferrite h-Lu0.6Sc0.4FeO3 with altermagnetic A2 spin ordering

Multiferroic hexagonal h-Lu0.6Sc0.4FeO3 single crystals with non-collinear spins were studied using the THz and Raman scattering spectroscopies and ellipsometry. Antiferromagnetic resonances, or magnons, were found at about 0.85 THz and 1.2 THz. These magnons harden as temperature increases and disappear above 130 K. This behavior is consistent with the magnetic susceptibility and a phase transition to a previously reported weak ferromagnetic state. A strong dichroism at the resonance with the AFM doublet has been observed at zero external magnetic field using both conventional circular polarization and THz vector vortex beams. This observation is attributed to the strong altermagnetic properties of h-Lu0.6Sc0.4FeO3 with a broken PT symmetry. The splitting of the magnon doublet in an external magnetic field applied long the c axis yields a g-factor of 3.0 for the Fe3+ ions. Raman spectra of the optical phonons revealed a Fano-type asymmetry due to their interaction with a continuum of polar excitations. Electronic transitions were studied with ellipsometry and the results were compared with the modelled using DFT+eDMFT.

cond-mat.other↗

arXiv2025

Cuddle-Fish: Exploring a Soft Floating Robot with Flapping Wings for Physical Interactions

Flying robots, such as quadrotor drones, offer new possibilities for human-robot interaction but often pose safety risks due to fast-spinning propellers, rigid structures, and noise. In contrast, lighter-than-air flapping-wing robots, inspired by animal movement, offer a soft, quiet, and touch-safe alternative. Building on these advantages, we present Cuddle-Fish, a soft flapping-wing floating robot designed for close-proximity interactions in indoor spaces. Through a user study with 24 participants, we explored their perceptions of the robot and experiences during a series of co-located demonstrations in which the robot moved near them. Results showed that participants felt safe, willingly engaged in touch-based interactions with the robot, and exhibited spontaneous affective behaviours, such as patting, stroking, hugging, and cheek-touching, without external prompting. They also reported positive emotional responses towards the robot. These findings suggest that the soft floating robot with flapping wings can serve as a novel and socially acceptable alternative to traditional rigid flying robots, opening new potential for applications in companionship, affective interaction, and play in everyday indoor environments.

cs.HC↗

arXiv2025

DNF-Intrinsic: Deterministic Noise-Free Diffusion for Indoor Inverse Rendering

Recent methods have shown that pre-trained diffusion models can be fine-tuned to enable generative inverse rendering by learning image-conditioned noise-to-intrinsic mapping. Despite their remarkable progress, they struggle to robustly produce high-quality results as the noise-to-intrinsic paradigm essentially utilizes noisy images with deteriorated structure and appearance for intrinsic prediction, while it is common knowledge that structure and appearance information in an image are crucial for inverse rendering. To address this issue, we present DNF-Intrinsic, a robust yet efficient inverse rendering approach fine-tuned from a pre-trained diffusion model, where we propose to take the source image rather than Gaussian noise as input to directly predict deterministic intrinsic properties via flow matching. Moreover, we design a generative renderer to constrain that the predicted intrinsic properties are physically faithful to the source image. Experiments on both synthetic and real-world datasets show that our method clearly outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2025

Structure-Guided Diffusion Models for High-Fidelity Portrait Shadow Removal

We present a diffusion-based portrait shadow removal approach that can robustly produce high-fidelity results. Unlike previous methods, we cast shadow removal as diffusion-based inpainting. To this end, we first train a shadow-independent structure extraction network on a real-world portrait dataset with various synthetic lighting conditions, which allows to generate a shadow-independent structure map including facial details while excluding the unwanted shadow boundaries. The structure map is then used as condition to train a structure-guided inpainting diffusion model for removing shadows in a generative manner. Finally, to restore the fine-scale details (e.g., eyelashes, moles and spots) that may not be captured by the structure map, we take the gradients inside the shadow regions as guidance and train a detail restoration diffusion model to refine the shadow removal result. Extensive experiments on the benchmark datasets show that our method clearly outperforms existing methods, and is effective to avoid previously common issues such as facial identity tampering, shadow residual, color distortion, structure blurring, and loss of details. Our code is available at https://github.com/wanchang-yu/Structure-Guided-Diffusion-for-Portrait-Shadow-Removal.

cs.CV↗

arXiv2025

Omni-Fusion of Spatial and Spectral for Hyperspectral Image Segmentation

Medical Hyperspectral Imaging (MHSI) has emerged as a promising tool for enhanced disease diagnosis, particularly in computational pathology, offering rich spectral information that aids in identifying subtle biochemical properties of tissues. Despite these advantages, effectively fusing both spatial-dimensional and spectral-dimensional information from MHSIs remains challenging due to its high dimensionality and spectral redundancy inherent characteristics. To solve the above challenges, we propose a novel spatial-spectral omni-fusion network for hyperspectral image segmentation, named as Omni-Fuse. Here, we introduce abundant cross-dimensional feature fusion operations, including a cross-dimensional enhancement module that refines both spatial and spectral features through bidirectional attention mechanisms, a spectral-guided spatial query selection to select the most spectral-related spatial feature as the query, and a two-stage cross-dimensional decoder which dynamically guide the model to focus on the selected spatial query. Despite of numerous attention blocks, Omni-Fuse remains efficient in execution. Experiments on two microscopic hyperspectral image datasets show that our approach can significantly improve the segmentation performance compared with the state-of-the-art methods, with over 5.73 percent improvement in DSC. Code available at: https://github.com/DeepMed-Lab-ECNU/Omni-Fuse.

cs.CV↗

arXiv2025

Domain Generalizable Portrait Style Transfer

This paper presents a portrait style transfer method that generalizes well to various different domains while enabling high-quality semantic-aligned stylization on regions including hair, eyes, eyelashes, skins, lips, and background. To this end, we propose to establish dense semantic correspondence between the given input and reference portraits based on a pre-trained model and a semantic adapter, with which we obtain a warped reference semantically aligned with the input. To ensure effective yet controllable style transfer, we devise an AdaIN-Wavelet transform to balance content preservation and stylization by blending low-frequency information of the warped reference with high-frequency information of the input in the latent space. A style adapter is also designed to provide style guidance from the warped reference. With the stylized latent from AdaIN-Wavelet transform, we employ a dual-conditional diffusion model that integrates a ControlNet recording high-frequency information and the style guidance to generate the final result. Extensive experiments demonstrate the superiority of our method. Our code and trained model are available at https://github.com/wangxb29/DGPST.

cs.CV↗

arXiv2025

Strongly Magnetically Tuned Coupling Strength and Nonlinearity in CrSBr Exciton-Polaritons

Two-dimensional van der Waals (vdW) magnetic semiconductors CrSBr offer an ideal platform to achieve exciton-polaritons correlated with magnetic orders for developing solid-state quantum, spintronic, and photonic devices. However, for the exciton-polaritons formed by lower-energy excitons (XL = 1.37 eV), the coupling strength and nonlinear optical response are almost inert to the external magnetic field. Here, we demonstrate robust strong coupling between higher-energy excitons (XH = 1.8 eV) and photons that persists up to room temperature, along with giant magnetic-field tunability. The Rabi splitting energy is tuned up to 100 meV within a moderate 0.45 T in-plane magnetic field due to changes in excitonic states during the spin transitions. Besides, we observe significantly enhanced polariton nonlinearity in the intermediate magnetic phase, which exhibits a distinct mode-number dependence and originates from magnon-assisted long-range attractive interactions and coupling strength reduction. These results advance the development of on-demand polariton platforms for spin-correlated quantum optoelectronics.

cond-mat.mtrl-sci↗

arXiv2025

Doping-induced Polyamorphic Transitions in Fluorite Oxides

Fluorite oxides such as HfO$_2$ exhibit rich and tunable phase behavior, making them promising candidates for next generation electronic devices. A key challenge is to design amorphous HfO$_2$-based high-$k$ materials with both structural and performance stability. Here, using molecular dynamics simulations supported by experimental measurements, we reveal that Ba doping stimulates a polyamorphic transition in HfO$_2$, yielding a semi-ordered amorphous (SA) phase characterized by disordered oxygens embedded within an ordered metal sublattice. We find that this phase arises from degenerate short-range symmetry breaking modes, consistent with Pauling's parsimony rule. Notably, the SA structure is thermodynamically stable and displays a wider bandgap and higher dielectric constant than conventional random-packing amorphous structure, owing to suppressed subgap states and increased Born effective charges. We further demonstrate that this structural motif generalizes to Ba-, Sr-, and Ca-doped HfO$_2$ and ZrO$_2$, establishing a broadly applicable strategy for designing high-performance amorphous dielectrics.

cond-mat.mtrl-sci↗

arXiv2025

Assessing Privacy Compliance of Android Third-Party SDKs

Third-party Software Development Kits (SDKs) are widely adopted in Android app development, to effortlessly accelerate development pipelines and enhance app functionality. However, this convenience raises substantial concerns about unauthorized access to users' privacy-sensitive information, which could be further abused for illegitimate purposes like user tracking or monetization. Our study offers a targeted analysis of user privacy protection among Android third-party SDKs, filling a critical gap in the Android software supply chain. It focuses on two aspects of their privacy practices, including data exfiltration and behavior-policy compliance (or privacy compliance), utilizing techniques of taint analysis and large language models. It covers 158 widely-used SDKs from two key SDK release platforms, the official one and a large alternative one. From them, we identified 338 instances of privacy data exfiltration. On the privacy compliance, our study reveals that more than 30% of the examined SDKs fail to provide a privacy policy to disclose their data handling practices. Among those that provide privacy policies, 37% of them over-collect user data, and 88% falsely claim access to sensitive data. We revisit the latest versions of the SDKs after 12 months. Our analysis demonstrates a persistent lack of improvement in these concerning trends. Based on our findings, we propose three actionable recommendations to mitigate the privacy leakage risks and enhance privacy protection for Android users. Our research not only serves as an urgent call for industry attention but also provides crucial insights for future regulatory interventions.

cs.CR↗

arXiv2025

Retrospective Memory for Camouflaged Object Detection

Camouflaged object detection (COD) primarily focuses on learning subtle yet discriminative representations from complex scenes. Existing methods predominantly follow the parametric feedforward architecture based on static visual representation modeling. However, they lack explicit mechanisms for acquiring historical context, limiting their adaptation and effectiveness in handling challenging camouflage scenes. In this paper, we propose a recall-augmented COD architecture, namely RetroMem, which dynamically modulates camouflage pattern perception and inference by integrating relevant historical knowledge into the process. Specifically, RetroMem employs a two-stage training paradigm consisting of a learning stage and a recall stage to construct, update, and utilize memory representations effectively. During the learning stage, we design a dense multi-scale adapter (DMA) to improve the pretrained encoder's capability to capture rich multi-scale visual information with very few trainable parameters, thereby providing foundational inferences. In the recall stage, we propose a dynamic memory mechanism (DMM) and an inference pattern reconstruction (IPR). These components fully leverage the latent relationships between learned knowledge and current sample context to reconstruct the inference of camouflage patterns, thereby significantly improving the model's understanding of camouflage scenes. Extensive experiments on several widely used datasets demonstrate that our RetroMem significantly outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2025

The 2D Materials Roadmap

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Braided zesting and its applications

We give a rigorous development of the construction of new braided fusion categories from a given category known as zesting. This method has been used in the past to provide categorifications of new fusion rule algebras, modular data, and minimal modular extensions of super-modular categories. Here we provide a complete obstruction theory and parameterization approach to the construction and illustrate its utility with several examples.

math.QA↗

arXiv2025

Superconductivity in trilayer nickelate La4Ni3O10 under pressure

Nickelate superconductors have attracted a great deal of attention over the past few decades due to their similar crystal and electronic structures with high-temperature cuprate superconductors. Here, we report the superconductivity in a pressurized Ruddlesden-Popper phase single crystal, La4Ni3O10 (n = 3), and its interplay with the density wave order in the phase diagram. With increasing pressure, the density wave order as indicated by the anomaly in the resistivity is progressively suppressed, followed by the emergence of the superconductivity around 25 K under I4/mmm space group. The susceptibility measurements confirm bulk superconductivity with a volume fraction exceeding 80%. Moreover, theoretical analysis unveils that antiferromagnetic (AFM) super-exchange interactions can serve as the effective pairing interaction for the emergence of superconductivity (SC) in pressurized La4Ni3O10. Our research provides a new platform for the investigation of the unconventional superconductivity mechanism in Ruddlesden-Popper trilayer perovskite nickelates.

cond-mat.supr-con↗

arXiv2025

Realizing modular data from centers of near-group categories

In this paper, we show the existence of a near-group category of type $\mathbb{Z} / 4\mathbb{Z} \times \mathbb{Z} / 4\mathbb{Z}+16$ and compute the modular data of its Drinfeld center. We prove that a modular data of rank $10$ can be obtained through condensation of the Drinfeld center of the near-group category $\mathbb{Z} / 4\mathbb{Z} \times \mathbb{Z} / 4\mathbb{Z}+16$, and it can also be realized as the Drinfeld center of a fusion category of rank $4$. Moreover, we compute the modular data for the Drinfeld center of a near-group category $\mathbb{Z} / 8\mathbb{Z}+8$ and show that the non-pointed factor of its condensation has the same modular data as the quantum group category $C(\mathfrak{g}_2, 4)$.

math.QA↗