arXiv subjects

Yang Qiu

Publications and source records attributed to Yang Qiu.

At least 19 recordsLinked to original sources

arXiv2026

Dzyaloshinskii-Moriya Gradients Unlock Topological Dimensional Reduction in Magnetic Hopfions

Three-dimensional magnetic solitons retain topological protection only while their spin field remains continuous. Here we show that chemical inhomogeneity can break this protection in a controlled way, converting a hopfion-like toroidal texture into an effectively two-dimensional skyrmion string. Tilt-dependent Lorentz transmission electron microscopy, electron energy-loss spectroscopy, and micromagnetic simulations of pristine, uniformly Y-doped, and nonuniformly Y-doped disordered TiO2 nanoparticles embedded in a FeCrNiMn host reveal two regimes. Uniform Y doping enriches Ti3+/oxygen-vacancy localization centers and stabilizes closed rings with Hopf invariant QH approximately 1. Nonuniform Y doping forms a Ti4+-rich, vacancy-depleted boundary that creates a sharp q=D/(2A) gradient and a weak-moment leakage channel. This coupled mismatch torque and continuity leakage split the ring, leaving a skyrmion-string remnant and a field-sensitive helicity texture.

cond-mat.other↗

arXiv2026

Arachne: Orchestrating Cascades for Efficient Text-to-Video Model Training

The rising demand for AI-generated videos is fueled by advances in large-scale Text-to-Video (T2V) models, trained on extensive datasets of video clips spanning diverse resolutions and durations. To address this data heterogeneity, current training methods often use a bucketing strategy that groups samples into discrete buckets for efficiency. However, this approach struggles to scale with compute and data volumes under static parallelism schemes, such as data and sequence parallelism, leading to significant workload imbalances and hardware under-utilization. In this paper, we present Arachne, a novel training framework for efficient T2V model training at scale. Arachne decomposes the training process into fine-grained computational units, called \textit{cascades}, orchestrating their distributed execution and synchronization across the cluster through coordinated spatial and temporal optimization. Our comprehensive evaluation demonstrates that Arachne reduces iteration time by up to 65\% over leading frameworks, exhibiting a positive scaling trend where its performance advantages amplify as training scale grows.

cs.DC↗

arXiv2026

MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson's Disease Gait Assessment

Gait-based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously. New sensors may arrive through device upgrades, protocol changes, or multi-center deployment, while historical patient data are often unavailable because of privacy and storage constraints. This modality-incremental setting faces three challenges: unreliable cross-modal distillation, modality-specific statistical shifts, and reduced plasticity after preservation. We propose MOSAIC, a compact continual learning framework. First, we identify the Toxic Teacher phenomenon and introduce Modality-Specific Warm-Up to stabilize newly learned modality representations before distillation. Second, we propose a statistics-decoupled MSBN architecture that isolates sensor statistics while maintaining a shared semantic backbone. Third, we design a curriculum-guided repulsive objective for Plasticity Recovery, preserving legacy knowledge while recovering modality-specific capacity. Experiments on three multimodal Parkinson's gait datasets show that MOSAIC improves final performance and mitigates forgetting. Project code is available at: https://github.com/minlinzeng/MOSAIC_Modality-Specific-Adaptation-for-Incremental-Continual-Learning-in-PD-Gait-Assessment.git

cs.AI↗

arXiv2026

Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization

Graph Neural Networks (GNNs) have achieved remarkable success across various graph-based tasks but remain highly sensitive to distribution shifts. In this work, we focus on a prevalent yet under-explored phenomenon in graph generalization, Minimal Shift Flip (MSF),where test samples that slightly deviate from the training distribution are abruptly misclassified. To interpret this phenomenon, we revisit MSF through the lens of Sharpness-Aware Minimization (SAM), which characterizes the local stability and sharpness of the loss landscape while providing a theoretical foundation for modeling generalization error. To quantify loss sharpness, we introduce the concept of Local Robust Radius, measuring the smallest perturbation required to flip a prediction and establishing a theoretical link between local stability and generalization. Building on this perspective, we further observe a continual decrease in the robust radius during training, indicating weakened local stability and an increasingly sharp loss landscape that gives rise to MSF. To jointly solve the MSF phenomenon and the intractability of radius, we develop an energy-based formulation that is theoretically proven to be monotonically correlated with the robust radius, offering a tractable and principled objective for modeling flatness and stability. Building on these insights, we propose an energy-driven generative augmentation framework (E2A) that leverages energy-guided latent perturbations to generate pseudo-OOD samples and enhance model generalization. Extensive experiments across multiple benchmarks demonstrate that E2A consistently improves graph OOD generalization, outperforming state-of-the-art baselines.

cs.LG↗

arXiv2025

Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization

Out-of-distribution generalization under distributional shifts remains a critical challenge for graph neural networks. Existing methods generally adopt the Invariant Risk Minimization (IRM) framework, requiring costly environment annotations or heuristically generated synthetic splits. To circumvent these limitations, in this work, we aim to develop an IRM-free method for capturing causal subgraphs. We first identify that causal subgraphs exhibit substantially smaller distributional variations than non-causal components across diverse environments, which we formalize as the Invariant Distribution Criterion and theoretically prove in this paper. Building on this criterion, we systematically uncover the quantitative relationship between distributional shift and representation norm for identifying the causal subgraph, and investigate its underlying mechanisms in depth. Finally, we propose an IRM-free method by introducing a norm-guided invariant distribution objective for causal subgraph discovery and prediction. Extensive experiments on two widely used benchmarks demonstrate that our method consistently outperforms state-of-the-art methods in graph generalization.

cs.LG↗

arXiv2025

Towards Relaxed Multimodal Inputs for Gait-based Parkinson's Disease Assessment

Parkinson's disease assessment has garnered growing interest in recent years, particularly with the advent of sensor data and machine learning techniques. Among these, multimodal approaches have demonstrated strong performance by effectively integrating complementary information from various data sources. However, two major limitations hinder their practical application: (1) the need to synchronize all modalities during training, and (2) the dependence on all modalities during inference. To address these issues, we propose the first Parkinson's assessment system that formulates multimodal learning as a multi-objective optimization (MOO) problem. This not only allows for more flexible modality requirements during both training and inference, but also handles modality collapse issue during multimodal information fusion. In addition, to mitigate the imbalance within individual modalities, we introduce a margin-based class rebalancing strategy to enhance category learning. We conduct extensive experiments on three public datasets under both synchronous and asynchronous settings. The results show that our framework-Towards Relaxed InPuts (TRIP)-achieves state-of-the-art performance, outperforming the best baselines by 16.48, 6.89, and 11.55 percentage points in the asynchronous setting, and by 4.86 and 2.30 percentage points in the synchronous setting, highlighting its effectiveness and adaptability.

cs.AI↗

arXiv2025

AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results

This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms for HDR image reconstruction from single LDR inputs, focusing on perceptual fidelity and numerical consistency. A total of \textbf{67} participants submitted \textbf{319} valid results, from which the best five teams were selected for detailed analysis. This report consolidates their methodologies and performance, with the lowest PU21-PSNR among the top entries reaching 29.22 dB. The analysis highlights innovative strategies for enhancing HDR reconstruction quality and establishes strong benchmarks to guide future research in inverse tone mapping.

cs.CV↗

arXiv2025

MIRAGE: Towards AI-Generated Image Detection in the Wild

The spreading of AI-generated images (AIGI), driven by advances in generative AI, poses a significant threat to information security and public trust. Existing AIGI detectors, while effective against images in clean laboratory settings, fail to generalize to in-the-wild scenarios. These real-world images are noisy, varying from ``obviously fake" images to realistic ones derived from multiple generative models and further edited for quality control. We address in-the-wild AIGI detection in this paper. We introduce Mirage, a challenging benchmark designed to emulate the complexity of in-the-wild AIGI. Mirage is constructed from two sources: (1) a large corpus of Internet-sourced AIGI verified by human experts, and (2) a synthesized dataset created through the collaboration between multiple expert generators, closely simulating the realistic AIGI in the wild. Building on this benchmark, we propose Mirage-R1, a vision-language model with heuristic-to-analytic reasoning, a reflective reasoning mechanism for AIGI detection. Mirage-R1 is trained in two stages: a supervised-fine-tuning cold start, followed by a reinforcement learning stage. By further adopting an inference-time adaptive thinking strategy, Mirage-R1 is able to provide either a quick judgment or a more robust and accurate conclusion, effectively balancing inference speed and performance. Extensive experiments show that our model leads state-of-the-art detectors by 5% and 10% on Mirage and the public benchmark, respectively. The benchmark and code will be made publicly available.

cs.CV↗

arXiv2025

Direct imaging of quantum interference and Non-Abelian entanglement in Hopfion: an magnetic soliton possess loop-like anyonic properties

This work provides the first experimental elucidation of quantum topological effects in individual hopfions, establishing their potential as building blocks for three-dimensional topological quantum spintronics. The observed Non-Abelian characteristics suggest pathways toward fault-tolerant quantum operations through controlled hopfion braiding in engineered magnetic metamaterials.

cond-mat.mes-hall↗

arXiv2025

An Intelligent and Privacy-Preserving Digital Twin Model for Aging-in-Place

The population of older adults is steadily increasing, with a strong preference for aging-in-place rather than moving to care facilities. Consequently, supporting this growing demographic has become a significant global challenge. However, facilitating successful aging-in-place is challenging, requiring consideration of multiple factors such as data privacy, health status monitoring, and living environments to improve health outcomes. In this paper, we propose an unobtrusive sensor system designed for installation in older adults' homes. Using data from the sensors, our system constructs a digital twin, a virtual representation of events and activities that occurred in the home. The system uses neural network models and decision rules to capture residents' activities and living environments. This digital twin enables continuous health monitoring by providing actionable insights into residents' well-being. Our system is designed to be low-cost and privacy-preserving, with the aim of providing green and safe monitoring for the health of older adults. We have successfully deployed our system in two homes over a time period of two months, and our findings demonstrate the feasibility and effectiveness of digital twin technology in supporting independent living for older adults. This study highlights that our system could revolutionize elder care by enabling personalized interventions, such as lifestyle adjustments, medical treatments, or modifications to the residential environment, to enhance health outcomes.

cs.CY↗

arXiv2025

Probing scattering of Raman phonons on magnetic and electronic excitations in pyrochlores Nd$_2$Zr$_2$O$_7$ and Nd$_2$Ir$_2$O$_7$

Magnetic rare earth atoms on pyrochlore lattice can produce such exotic magnetic states as spin ice and quantum spin ice. These states are a result of the frustration in the pyrochlore lattice, as well as crystal field degrees of freedom of rare earth atoms, and their interactions with the lattice. Raman scattering spectroscopy, which possess high spectral resolution and can easily access broad energy and temperature ranges, is an optimum tool to study these excitations and their interactions. In this work we follow Raman scattering of zone center phonons and crystal field excitations of Nd$^{3+}$ in Nd$_2$Zr$_2$O$_7$ and Nd$_2$Ir$_2$O$_7$ in the temperature range where these materials are paramagnetic. A comparison between an insulating Nd$_2$Zr$_2$O$_7$ and semimetallic Nd$_2$Ir$_2$O$_7$ materials allow us to distinguish between scattering of phonons on other phonons, crystal field excitations, and electrons, highlighting interactions between these degrees of freedom.

cond-mat.str-el↗

arXiv2024

Stable Andrews-Curtis Conjecture via Fake Surfaces and Zeeman Conjecture

We propose an induction scheme that aims at establishing the stable Andrews-Curtis conjecture in the affirmative. The stable Andrews-Curtis conjecture is equivalent to the conjecture that every contractible fake surface is 3-deformable to a point. We prove that every contractible fake surface of complexity less than 6 is 3-deformable to a point by induction.

math.GT↗

arXiv2024

Acousto-optic modulation based on an AlScN microring resonator for microwave-to-optical conversion

Acoustic-optic (AO) modulation is critical for microwave and optical signal processing, computing and networking. Challenges remain to integrate AO devices on-chip using fabrication process compatible with complementary metal-oxide-semiconductor (CMOS) technology. This work presents the demonstration of an AO modulator exploiting a microring resonator (MRR) based on thin-film aluminum scandium nitride (AlScN) photonic platform. Leveraging the high piezoelectric properties of AlScN, an MRR is employed with interdigital transducer (IDT) inside to couple microwave signals into acoustic resonant modes, enabling efficient by-directional optical modulation in the MRR. The fabricated MRR exhibits an optical loaded quality factor (Q) of 1.8*e4 at the optical L-band for the TE00 mode. A low effective half-wave voltage Vpi of 1.21 V is achieved, corresponding to a VpiL of 0.0242 Vcm, along with an optomechanical single-photon coupling strength g0 of 0.43 kHz between the 2.11 GHz acoustic mode and the TE00 optical mode. The device shows potential for applications in microwave photonics.

physics.optics↗

arXiv2024

What makes math problems hard for reinforcement learning: a case study

Using a long-standing conjecture from combinatorial group theory, we explore, from multiple perspectives, the challenges of finding rare instances carrying disproportionately high rewards. Based on lessons learned in the context defined by the Andrews-Curtis conjecture, we propose algorithmic enhancements and a topological hardness measure with implications for a broad class of search problems. As part of our study, we also address several open mathematical questions. Notably, we demonstrate the length reducibility of all but two presentations in the Akbulut-Kirby series (1981), and resolve various potential counterexamples in the Miller-Schupp series (1991), including three infinite subfamilies.

cs.LG↗

arXiv2024

PAGE: Parametric Generative Explainer for Graph Neural Network

This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitating prior knowledge or internal details. Specifically, we train the auto-encoder to generate explanatory substructures by designing appropriate training strategy. Due to the dimensionality reduction of features in the latent space of the auto-encoder, it becomes easier to extract causal features leading to the model's output, which can be easily employed to generate explanations. To accomplish this, we introduce an additional discriminator to capture the causality between latent causal features and the model's output. By designing appropriate optimization objectives, the well-trained discriminator can be employed to constrain the encoder in generating enhanced causal features. Finally, these features are mapped to substructures of the input graph through the decoder to serve as explanations. Compared to existing methods, PAGE operates at the sample scale rather than nodes or edges, eliminating the need for perturbation or encoding processes as seen in previous methods. Experimental results on both artificially synthesized and real-world datasets demonstrate that our approach not only exhibits the highest faithfulness and accuracy but also significantly outperforms baseline models in terms of efficiency.

cs.LG↗

arXiv2024

User Behavior Analysis and Clustering in a MMO Mobile Game: Insights and Recommendations

This study presents a comprehensive analysis of user behavior and clustering in a popular mobile battle royale game, employing temporal and static data mining techniques to uncover distinct player segments. Our methodology encompasses time series K-means clustering, graph-based algorithms (DeepWalk and LINE), and static attribute clustering, visualized through innovative hybrid charts. Key findings reveal significant variations in player engagement, skill levels, and social interactions across five primary user segments, ranging from highly active and skilled players to inactive or new users. We also analyze the impact of external factors on user retention and the network structure within clusters, uncovering correlations between cluster cohesion and player activity levels. This research provides valuable insights for game developers and marketers, offering data-driven recommendations for personalized game experiences, targeted marketing strategies, and improved player retention in online gaming environments.

cs.SI↗

arXiv2024

Classification of Cellular Fake Surfaces

Generic polyhedra are interesting mathematical objects to study in their own right. In this paper, we initialize a systematic study of two-dimensional generic polyhedra with an eye towards applications to low-dimensional topology, especially the Andrews-Curtis and Zeeman conjectures. After recalling the basic notions of generic polyhedra and fake surfaces, we derive some interesting properties of fake surfaces. Our main result is a complete classification of acyclic cellular fake surfaces up to complexity 4 and a classification of acyclic cellular fake surfaces without small disks of complexity 5. From this classification, we prove the contractibility conjecture for acyclic cellular fake surfaces of complexity 4, and the embedded disk conjecture up to complexity 5. We provide evidence for the conjectures that the probability of being a spine among fake surfaces is 0 and that every contractible fake surface has an embedded disk.

math.GT↗

arXiv2024

Silicon-integrated scandium-doped aluminum nitride electro-optic modulator

Scandium-doped aluminum nitride (AlScN) with an asymmetric hexagonal wurtzite structure exhibits enhanced second-order nonlinear and piezoelectric properties compared to aluminum nitride (AlN), while maintaining a relatively large bandgap. It provides a promising platform for photonic integration and facilitates the seamless integration of passive and active functional devices. Here, we present the design, fabrication, and characterization of AlScN EO micro-ring modulators, introducing active functionalities to the chip-scale AlScN platform. These waveguide-integrated EO modulators employ sputtered AlScN thin films as the light-guiding medium, and the entire fabrication process is compatible with complementary metal oxide semiconductor (CMOS) technology. We characterize the high-frequency performance of an AlScN modulator for the first time, extracting a maximum in-device effective EO coefficient of 2.86 pm/V at 12 GHz. The devices show a minimum half-wave voltage-length product of 3.12 V*cm and a 3-dB modulation bandwidth of approximately 22 GHz. Our work provides a promising modulation scheme for cost-effective silicon-integrated photonics systems.

physics.app-ph↗