arXiv subjects

Ziwen Wang

Publications and source records attributed to Ziwen Wang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Dimension Scaling SR-Net for Super-Resolution Radar Range Profiles

High-resolution radar range profile (RRP) is crucial for accurate target recognition and scene perception. To get a high-resolution RRP, many methods have been developed, such as multiple signal classification (MUSIC), orthogonal matching pursuit (OMP), and a few deep learning-based approaches. Although they break through the Rayleigh resolution limit determined by radar signal bandwidth, these methods either get limited super-resolution capability or work well just in high signal to noise ratio (SNR) scenarios. To overcome these limitations, in this paper, an interpretable unfolded neural network for super-resolution RRP (DSSR-Net) is proposed by integrating the advantages of both model-guided and data-driven models. Specifically, DSSR-Net is designed based on a sparse representation model with dimension scaling, and then trained on a training dataset. Through dimension scaling, DSSR-Net lifts the radar signal into high-dimensional space to extract subtle features of closely spaced objects and suppress the noise of the high-dimensional features. It improves the super-resolving power of closely spaced objects and lowers the SNR requirement of radar signals compared to existing methods. The superiority of the proposed algorithm for super-resolution RRP reconstruction is verified via experiments with both synthetic and measured data.

eess.SP↗

arXiv2025

DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects

6D pose estimation of textureless objects is valuable for industrial robotic applications, yet remains challenging due to the frequent loss of depth information. Current multi-view methods either rely on depth data or insufficiently exploit multi-view geometric cues, limiting their performance. In this paper, we propose DKPMV, a pipeline that achieves dense keypoint-level fusion using only multi-view RGB images as input. We design a three-stage progressive pose optimization strategy that leverages dense multi-view keypoint geometry information. To enable effective dense keypoint fusion, we enhance the keypoint network with attentional aggregation and symmetry-aware training, improving prediction accuracy and resolving ambiguities on symmetric objects. Extensive experiments on the ROBI dataset demonstrate that DKPMV outperforms state-of-the-art multi-view RGB approaches and even surpasses the RGB-D methods in the majority of cases. The code will be available soon.

cs.CV↗

arXiv2025

RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters

Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in behavior influenced by terrain types or individual body shapes. This results in the failure to accurately simulate the escape of people in the real world. In this paper, aligned with the sensory-decision-motor (SDM) flow of the human brain, we propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor. This framework allows multiple agents to move in parallel and is suitable for various scenarios, with dynamic crowd awareness. Additionally, we introduce Part-level Force Visualization to assist in evacuation analysis. Experimental results demonstrate that our framework supports dynamic trajectory planning and personalized behavior for each agent throughout the evacuation process, and is compatible with uneven terrain. Visually, our method generates evacuation results that are more realistic and plausible, providing enhanced insights for crowd simulation. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/RESCUE.

cs.CV↗

arXiv2025

Design, synthesis, and physical properties of the intergrowth compound Eu$_2$CuZn$_2$As$_3$

The rational combination of existing magnetic topological compounds presents a promising route for designing new topological materials. We report the synthesis and comprehensive characterization of the layered quaternary intergrowth compound Eu$_2$CuZn$_2$As$_3$, which combines structural units of two known magnetic topological materials, EuCuAs and EuZn$_2$As$_2$. Eu$_2$CuZn$_2$As$_3$ exhibits an antiferromagnetic ground state with successive magnetic transitions: quasi-two-dimensional ordering at $T_\mathrm{M} = 29.3$\,K, long-range antiferromagnetic ordering at $T_\mathrm{N} = 19$\,K, and spin-reorientation at $T_\mathrm{SR} = 16.3$\,K. The stepwise magnetic transitions manifest as plateau-like anomalies in the heat capacity. These transitions originate from multiple superexchange pathways and periodic variation of interplane Eu-Eu distances in the intergrowth structure. Charge transport shows a pronounced resistivity increase above $T_\mathrm{N}$ followed by minimal change below the ordering temperature. Magnetic fields rapidly suppress this resistivity rise, yielding significant negative magnetoresistance. Remarkably, Eu$_2$CuZn$_2$As$_3$ inherits the nonlinear anomalous Hall effect characteristic of its parent compounds. Energy evaluations of collinear spin configurations reveal a lowest-energy state with ferromagnetic coupling between Eu planes in EuCuAs units while maintaining antiferromagnetic coupling within EuZn$_2$As$_2$ units. The corresponding electronic structure displays potentially topologically nontrivial features. Our work demonstrates the efficacy of structural hybridization for discovering novel magnetic topological materials and establishes a general strategy for materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Segment Any Vehicle: Semantic and Visual Context Driven SAM and A Benchmark

With the rapid advancement of autonomous driving, vehicle perception, particularly detection and segmentation, has placed increasingly higher demands on algorithmic performance. Pre-trained large segmentation models, especially Segment Anything Model (SAM), have sparked significant interest and inspired new research directions in artificial intelligence. However, SAM cannot be directly applied to the fine-grained task of vehicle part segmentation, as its text-prompted segmentation functionality is not publicly accessible, and the mask regions generated by its default mode lack semantic labels, limiting its utility in structured, category-specific segmentation tasks. To address these limitations, we propose SAV, a novel framework comprising three core components: a SAM-based encoder-decoder, a vehicle part knowledge graph, and a context sample retrieval encoding module. The knowledge graph explicitly models the spatial and geometric relationships among vehicle parts through a structured ontology, effectively encoding prior structural knowledge. Meanwhile, the context retrieval module enhances segmentation by identifying and leveraging visually similar vehicle instances from training data, providing rich contextual priors for improved generalization. Furthermore, we introduce a new large-scale benchmark dataset for vehicle part segmentation, named VehicleSeg10K, which contains 11,665 high-quality pixel-level annotations across diverse scenes and viewpoints. We conduct comprehensive experiments on this dataset and two other datasets, benchmarking multiple representative baselines to establish a solid foundation for future research and comparison. % Both the dataset and source code of this paper will be released upon acceptance. Both the dataset and source code of this paper will be released on https://github.com/Event-AHU/SAV

cs.CV↗

arXiv2025

FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework

Online intelligent education platforms have generated a vast amount of distributed student learning data. This influx of data presents opportunities for cognitive diagnosis (CD) to assess students' mastery of knowledge concepts while also raising significant data privacy and security challenges. To cope with this issue, federated learning (FL) becomes a promising solution by jointly training models across multiple local clients without sharing their original data. However, the data quality problem, caused by the ability differences and educational context differences between different groups/schools of students, further poses a challenge to the fairness of models. To address this challenge, this paper proposes a fairness-aware federated cognitive diagnosis framework (FedCD) to jointly train CD models built upon a novel parameter decoupling-based personalization strategy, preserving privacy of data and achieving precise and fair diagnosis of students on each client. As an FL paradigm, FedCD trains a local CD model for the students in each client based on its local student learning data, and each client uploads its partial model parameters to the central server for parameter aggregation according to the devised innovative personalization strategy. The main idea of this strategy is to decouple model parameters into two parts: the first is used as locally personalized parameters, containing diagnostic function-related model parameters, to diagnose each client's students fairly; the second is the globally shared parameters across clients and the server, containing exercise embedding parameters, which are updated via fairness-aware aggregation, to alleviate inter-school unfairness. Experiments on three real-world datasets demonstrate the effectiveness of the proposed FedCD framework and the personalization strategy compared to five FL approaches under three CD models.

cs.LG↗

arXiv2025

Autferroicity: concept, candidates, and applications

Autferroicity is a newly proposed form of hybrid ferroicity, which is a sister branch of multiferroicity. It is characterized by the mutually exclusive magnetic and polar phases within a single system, giving a unique seesaw-type magnetoelectric coupling. This perspective provides a theoretical overview of its underlying concept, phase diagram characteristics, and representative candidates such as Ti-based trichalcogenide monolayers, while also highlighting its potential applications in nonvolatile memory devices and true random number generation.

cond-mat.mtrl-sci↗

arXiv2025

Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.

cs.HC↗

arXiv2025

JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence Clustering

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling gene expression analysis at the individual cell level. Clustering allows for the identification of cell types and the further discovery of intrinsic patterns in single-cell data. However, the high dimensionality and sparsity of scRNA-seq data continue to challenge existing clustering models. In this paper, we introduce JojoSCL, a novel self-supervised contrastive learning framework for scRNA-seq clustering. By incorporating a shrinkage estimator based on hierarchical Bayesian estimation, which adjusts gene expression estimates towards more reliable cluster centroids to reduce intra-cluster dispersion, and optimized using Stein's Unbiased Risk Estimate (SURE), JojoSCL refines both instance-level and cluster-level contrastive learning. Experiments on ten scRNA-seq datasets substantiate that JojoSCL consistently outperforms prevalent clustering methods, with further validation of its practicality through robustness analysis and ablation studies. JojoSCL's code is available at: https://github.com/ziwenwang28/JojoSCL.

cs.LG↗

arXiv2025

Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese

Recent advances in large language models (LLMs) have significantly improved text-to-speech (TTS) systems, enhancing control over speech style, naturalness, and emotional expression, which brings TTS Systems closer to human-level performance. Although the Mean Opinion Score (MOS) remains the standard for TTS System evaluation, it suffers from subjectivity, environmental inconsistencies, and limited interpretability. Existing evaluation datasets also lack a multi-dimensional design, often neglecting factors such as speaking styles, context diversity, and trap utterances, which is particularly evident in Chinese TTS evaluation. To address these challenges, we introduce the Audio Turing Test (ATT), a multi-dimensional Chinese corpus dataset ATT-Corpus paired with a simple, Turing-Test-inspired evaluation protocol. Instead of relying on complex MOS scales or direct model comparisons, ATT asks evaluators to judge whether a voice sounds human. This simplification reduces rating bias and improves evaluation robustness. To further support rapid model development, we also finetune Qwen2-Audio-Instruct with human judgment data as Auto-ATT for automatic evaluation. Experimental results show that ATT effectively differentiates models across specific capability dimensions using its multi-dimensional design. Auto-ATT also demonstrates strong alignment with human evaluations, confirming its value as a fast and reliable assessment tool. The white-box ATT-Corpus and Auto-ATT can be found in ATT Hugging Face Collection (https://huggingface.co/collections/meituan/audio-turing-test-682446320368164faeaf38a4).

cs.SD↗

arXiv2025

Polarization switching in sliding ferroelectrics: the roles of fluctuation and domain wall

Sliding ferroelectricity is highly attractive for its low energy barriers and fatigue resistance. As the origin of these exotic properties, its unconventional switching dynamics remains poorly understood: how an electric field drives a perpendicular sliding? Taking $h$-BN bilayer as a model system, its switching dynamics is studied using \textit{ab initio} calculations. The off-diagonal Born effective charge leads to the perpendicular relationship between the electric field and ionic movements. Interestingly, the rules of intrinsic coercive field are distinct between $h$-BN bilayer and conventional ferroelectrics. For $h$-BN bilayer, any perturbation breaking the in-plane symmetry plays a key role to assist the avalanche-like switching dynamics. Moreover, the exotic large off-diagonal Born effective charge near the $P=0$ intermediate state results in a wriggling motion of domain walls in $h$-BN bilayer. Our results reveal the key factors in the ferroelectric switching of sliding ferroelectrics at room temperature.

cond-mat.mtrl-sci↗

arXiv2025

Structural design and multiple magnetic orderings of the intergrowth compound Eu$_2$CuMn$_2$P$_3$

We report the design, synthesis, crystal structure, and physical properties of a layered intergrowth compound, Eu$_2$CuMn$_2$P$_3$. The structure of Eu$_2$CuMn$_2$P$_3$ features an alternating arrangement of hexagonal EuCuP block layers and trigonal EuMn$_2$P$_2$ block layers, interconnected through shared Eu planes. This structural hybridization leads to multiple magnetic orderings in Eu$_2$CuMn$_2$P$_3$: weak antiferromagnetic (AFM) ordering of Mn at $T_\mathrm{N}^\mathrm{Mn}$ = 80 K, AFM ordering of Eu at $T_\mathrm{N}^\mathrm{Eu}$ = 29 K, a spin-reorientation transition at $T_\mathrm{SR}$ = 14.5 K, and weak ferromagnetism below $T_\mathrm{N}^\mathrm{Mn}$. The spin configurations at different temperature regions were discussed based on the calculations of magnetic energies for various collinear arrangements. Resistivity measurements reveal a pronounced transition peak at $T_\mathrm{N}^\mathrm{Eu}$, which is suppressed in the presence of a magnetic field, resulting in a significant negative magnetoresistance effect. The computed semimetallic band structure, characterized by a small density of states at the Fermi level, aligns well with experimental observations. The successful synthesis of Eu$_2$CuMn$_2$P$_3$ and its fascinating magnetic properties highlight the effectiveness of our block-layer design strategy. By assembling magnetic block layers of compounds with compatible crystal symmetries and closely matched lattice parameters, this approach opens exciting avenues for discovering layered materials with unique magnetic behaviors.

cond-mat.mtrl-sci↗

arXiv2025

Intersection theory, relative cohomology and the Feynman parametrization

We present a novel approach for loop integral reduction in the Feynman parametrization using intersection theory and relative cohomology. In this framework, Feynman integrals correspond to boundary-supported differential forms in the language of relative cohomology. The integral reduction can then be achieved by computing intersection numbers. We apply our method in several examples to demonstrate its correctness, and discuss the subtleties in certain degenerate limits.

hep-th↗

arXiv2025

ACPO: AI-Enabled Compiler Framework

The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler. This is an ideal opportunity to apply machine-learning models to speed up the tuning process; while this realization has been around since the late 90s, only recent advancements in ML enabled a practical application of ML to compilers as an end-to-end framework. This paper presents ACPO: An AI-Enabled Compiler Framework, a novel framework that provides LLVM with simple and comprehensive tools to benefit from employing ML models for different optimization passes. We first showcase the high-level view, class hierarchy, and functionalities of ACPO and subsequently, demonstrate \taco{a couple of use cases of ACPO by ML-enabling the Loop Unroll and Function Inlining passes used in LLVM's O3. and finally, describe how ACPO can be leveraged to optimize other passes. Experimental results reveal that the ACPO model for Loop Unroll can gain on average 4%, 3%, 5.4%, and 0.2% compared to LLVM's vanilla O3 optimization when deployed on Polybench, Coral-2, CoreMark, and Graph-500, respectively. Furthermore, by including both Function Inlining and Loop Unroll models, ACPO can provide a combined speedup of 4.5% on Polybench and 2.4% on Cbench when compared with LLVM's O3, respectively.

cs.PL↗

arXiv2024

Unusual magnetic and transport properties in the Zintl phase Eu$_{11}$Zn$_6$As$_{12}$

Narrow-gap rare-earth Zintl phases frequently exhibit fascinating physical phenomena due to their various crystal structures, complex magnetic properties, and tunable transport behaviors. Here we report the synthesis, magnetic, thermodynamic, and transport properties of a Eu-containing Zintl arsenide, Eu$_{11}$Zn$_6$As$_{12}$, which consists of infinite chains of Eu cations and anionic frameworks constructed from corner-sharing ZnAs$_4$ tetrahedra. Eu$_{11}$Zn$_6$As$_{12}$ exhibits complicated magnetic behavior owing to intricate exchange interactions mediated by the discrete anionic fragments. Two long-range magnetic transitions at 22 K ($T_\mathrm{N}$) and 9 K ($T^*$), as well as exceptionally strong ferromagnetic fluctuations around 29 K ($T_\mathrm{F}$), are indicated by the susceptibility, heat capacity and resistivity measurements. Besides, Eu$_{11}$Zn$_6$As$_{12}$ displays metallic behavior, attributable to the hole carriers doped by slight Eu vacancies or the mixed valence of Eu$^{2+}$ and Eu$^{3+}$. A prominent resistivity peak occurs around $T_\mathrm{N}$, which is rapidly suppressed by the applied field, leading to a prominent negative magnetoresistance effect. A resistivity hysteresis is observed below 5 K, caused by a small net ferromagnetic component. Our study presents the distinct magnetic and transport properties of Eu$_{11}$Zn$_6$As$_{12}$, and further experiments are required to elucidate the origin of these novel behaviors. Moreover, our findings demonstrate that Eu-based Zintl phases are a fertile ground to study the interplay between magnetism and charge transport.

cond-mat.mtrl-sci↗

arXiv2024

DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned; the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at \textcolor{red}{\url{https://github.com/BIMK/Intelligent-Education/tree/main/DisenGCD}}.

cs.LG↗

arXiv2024

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis

Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts. Transfer learning (TL), a machine learning technique, emerges as a powerful solution by utilizing knowledge from pre-trained models to enhance the performance of new models, offering promise across various healthcare domains. Despite its conceptual origins in the 1990s, the application of TL in medical research has remained limited, especially beyond image analysis. In our review of TL applications in structured clinical and biomedical data, we screened 3,515 papers, with 55 meeting the inclusion criteria. Among these, only 2% (one out of 55) utilized external studies, and 7% (four out of 55) addressed scenarios involving multi-site collaborations with privacy constraints. To achieve actionable TL with structured medical data while addressing regional disparities, inequality, and privacy constraints in healthcare research, we advocate for the careful identification of appropriate source data and models, the selection of suitable TL frameworks, and the validation of TL models with proper baselines.

cs.LG↗

arXiv2024

Manipulating magnetism and transport properties of EuCd$_2$P$_2$ with a low carrier concentration

Materials that exhibit strongly coupled magnetic order and electronic properties are crucial for both fundamental research and technological applications. However, finding a material that not only shows remarkable magnetoresistive responses but also has an easily tunable ground state remains a challenge. Here, we report successful manipulation of the magnetic and transport properties of EuCd$_2$P$_2$, which is transformed from an A-type antiferromagnet ($T_\mathrm{N}$ = 11 K) exhibiting colossal magnetoresistance into a ferromagnet ($T_\mathrm{C}$ = 47 K) with metallic behavior. The dramatic alteration results from a low hole concentration of $10^{19}$ cm$^{-3}$ induced by changing the growth conditions. Electronic structure and total energy calculations confirm the tunability of magnetism with a small carrier concentration for EuCd$_2$P$_2$. It is feasible to switch between the magnetic states by using field-effect to control the carrier density, thereby changing the magneto-electronic response. The controllable magnetism and electrical transport of EuCd$_2$P$_2$ make it a potential candidate for spintronics.

cond-mat.mtrl-sci↗