arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 505 records · Page 28Linked to original sources

arXiv2025

B(E2) anomaly and triaxial deformation within a two-fluid SU(3) symmetry

The correlation between $B(E2)$ structure and triaxial deformation has been investigated within the framework of the proton-neutron boson model. The analysis reveals that the distinctive feature, characterized by $B(E2;4_1^+\rightarrow2_1^+)/B(E2;2_1^+\rightarrow0_1^+)<1.0$ along with $E(4_1^+)/E(2_1^+)>2.0$, can emerge from the triaxial SU(3) symmetry inherent in two-fluid boson systems, attributed to band-mixing effects. This suggests a symmetry-based understanding of the anomalous $E2$ transitions observed in experiments.

nucl-th↗

arXiv2025

B(E2) anomaly and triaxial deformation in the interacting boson model

The influences of triaxial dynamics on the anomalous $E2$ transitional behaviors in the interacting boson model (IBM) have been comprehensively examined without symmetry restrictions. Specifically, different triaxial schemes in the IBM have been analyzed using a general procedure for deriving triaxial rotor modes based on two distinct approaches. The results indicate that the $B(E2)$ anomaly feature, characterized by $B_{4/2}=B(E2;4_1^+\rightarrow2_1^+)/B(E2;2_1^+\rightarrow0_1^+)<1.0$, can occur in all triaxial schemes due to strong band-mixing effects. This finding is further tested in describing the available data for $^{172}$Pt,~$^{168,170}$Os and $^{166}$W using the model constructed upon the extended consistent-$Q$ formula, providing a simple theoretical explanation of the depressed $B_{4/2}$ values observed in the experiments.

nucl-th↗

arXiv2025

Algebraic description of the triaxially to axially rotational shape phase transition

Within the framework of the interacting boson model, we propose a novel algebraic scheme to describe spin-dependent structural evolutions in triaxial nuclei. Our analysis demonstrates that a triaxially to axially rotational shape phase transition can be induced by the high-order correction which is microscopically motivated by the pairing interaction on the $γ$-unstable rotation. This prescription is further applied to describe the structural evolutions along the yrast sequences of $^{132}$Ba and $^{130}$Xe, providing an exemplary case for demonstrating a unified explanation of both low-energy collective structures and high-spin phenomena in O(6)-like nuclei.

nucl-th↗

arXiv2025

MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation

Recommender systems traditionally represent items using unique identifiers (ItemIDs), but this approach struggles with large, dynamic item corpora and sparse long-tail data, limiting scalability and generalization. Semantic IDs, derived from multimodal content such as text and images, offer a promising alternative by mapping items into a shared semantic space, enabling knowledge transfer and improving recommendations for new or rare items. However, existing methods face two key challenges: (1) balancing cross-modal synergy with modality-specific uniqueness, and (2) bridging the semantic-behavioral gap, where semantic representations may misalign with actual user preferences. To address these challenges, we propose Multimodal Mixture-of-Quantization (MMQ), a two-stage framework that trains a novel multimodal tokenizer. First, a shared-specific tokenizer leverages a multi-expert architecture with modality-specific and modality-shared experts, using orthogonal regularization to capture comprehensive multimodal information. Second, behavior-aware fine-tuning dynamically adapts semantic IDs to downstream recommendation objectives while preserving modality information through a multimodal reconstruction loss. Extensive offline experiments and online A/B tests demonstrate that MMQ effectively unifies multimodal synergy, specificity, and behavioral adaptation, providing a scalable and versatile solution for both generative retrieval and discriminative ranking tasks.

cs.IR↗

arXiv2025

ASAudio: A Survey of Advanced Spatial Audio Research

With the rapid development of spatial audio technologies today, applications in AR, VR, and other scenarios have garnered extensive attention. Unlike traditional mono sound, spatial audio offers a more realistic and immersive auditory experience. Despite notable progress in the field, there remains a lack of comprehensive surveys that systematically organize and analyze these methods and their underlying technologies. In this paper, we provide a comprehensive overview of spatial audio and systematically review recent literature in the area. To address this, we chronologically outlining existing work related to spatial audio and categorize these studies based on input-output representations, as well as generation and understanding tasks, thereby summarizing various research aspects of spatial audio. In addition, we review related datasets, evaluation metrics, and benchmarks, offering insights from both training and evaluation perspectives. Related materials are available at https://github.com/dieKarotte/ASAudio.

eess.AS↗

arXiv2025

Generalizable Engagement Estimation in Conversation via Domain Prompting and Parallel Attention

Accurate engagement estimation is essential for adaptive human-computer interaction systems, yet robust deployment is hindered by poor generalizability across diverse domains and challenges in modeling complex interaction dynamics.To tackle these issues, we propose DAPA (Domain-Adaptive Parallel Attention), a novel framework for generalizable conversational engagement modeling. DAPA introduces a Domain Prompting mechanism by prepending learnable domain-specific vectors to the input, explicitly conditioning the model on the data's origin to facilitate domain-aware adaptation while preserving generalizable engagement representations. To capture interactional synchrony, the framework also incorporates a Parallel Cross-Attention module that explicitly aligns reactive (forward BiLSTM) and anticipatory (backward BiLSTM) states between participants.Extensive experiments demonstrate that DAPA establishes a new state-of-the-art performance on several cross-cultural and cross-linguistic benchmarks, notably achieving an absolute improvement of 0.45 in Concordance Correlation Coefficient (CCC) over a strong baseline on the NoXi-J test set. The superiority of our method was also confirmed by winning the first place in the Multi-Domain Engagement Estimation Challenge at MultiMediate'25.

cs.CV↗

arXiv2025

Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework

With the emergence of e-commerce, the recommendations provided by commercial platforms must adapt to diverse scenarios to accommodate users' varying shopping preferences. Current methods typically use a unified framework to offer personalized recommendations for different scenarios. However, they often employ shared bottom representations, which partially hinders the model's capacity to capture scenario uniqueness. Ideally, users and items should exhibit specific characteristics in different scenarios, prompting the need to learn scenario-specific representations to differentiate scenarios. Yet, variations in user and item interactions across scenarios lead to data sparsity issues, impeding the acquisition of scenario-specific representations. To learn robust scenario-specific representations, we introduce a Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework (GSVR) that can be directly applied to existing multi-scenario methods. Specifically, considering the uncertainty stemming from limited samples, our approach employs a probabilistic model to generate scenario-specific distributions for each user and item in each scenario, estimated through variational inference (VI). Additionally, we introduce the global knowledge-aware multinomial distributions as prior knowledge to regulate the learning of the posterior user and item distributions, ensuring similarities among distributions for users with akin interests and items with similar side information. This mitigates the risk of users or items with fewer records being overwhelmed in sparse scenarios. Extensive experimental results affirm the efficacy of GSVR in assisting existing multi-scenario recommendation methods in learning more robust representations.

cs.IR↗

arXiv2025

Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion

Traditional recommendation methods rely on correlating the embedding vectors of item IDs to capture implicit collaborative filtering signals to model the user's interest in the target item. Consequently, traditional ID-based methods often encounter data sparsity problems stemming from the sparse nature of ID features. To alleviate the problem of item ID sparsity, recommendation models incorporate multimodal item information to enhance recommendation accuracy. However, existing multimodal recommendation methods typically employ early fusion approaches, which focus primarily on combining text and image features, while neglecting the contextual influence of user behavior sequences. This oversight prevents dynamic adaptation of multimodal interest representations based on behavioral patterns, consequently restricting the model's capacity to effectively capture user multimodal interests. Therefore, this paper proposes the Distribution-Guided Multimodal-Interest Auto-Encoder (DMAE), which achieves the cross fusion of user multimodal interest at the behavioral level.Ultimately, extensive experiments demonstrate the superiority of DMAE.

cs.IR↗

arXiv2025

Adaptively Clustering Neighbor Elements for Image-Text Generation

We propose a novel Transformer-based image-to-text generation model termed as \textbf{ACF} that adaptively clusters vision patches into object regions and language words into phrases to implicitly learn object-phrase alignments for better visual-text coherence. To achieve this, we design a novel self-attention layer that applies self-attention over the elements in a local cluster window instead of the whole sequence. The window size is softly decided by a clustering matrix that is calculated by the current input data and thus this process is adaptive. By stacking these revised self-attention layers to construct ACF, the small clusters in the lower layers can be grouped into a bigger cluster, \eg vision/language. ACF clusters small objects/phrases into bigger ones. In this gradual clustering process, a parsing tree is generated which embeds the hierarchical knowledge of the input sequence. As a result, by using ACF to build the vision encoder and language decoder, the hierarchical object-phrase alignments are embedded and then transferred from vision to language domains in two popular image-to-text tasks: Image captioning and Visual Question Answering. The experiment results demonstrate the effectiveness of ACF, which outperforms most SOTA captioning and VQA models and achieves comparable scores compared with some large-scale pre-trained models. Our code is available \href{https://github.com/ZihuaEvan/ACFModel/}{[here]}.

cs.CV↗

arXiv2025

UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction

Dentocraniofacial hard tissue defects profoundly affect patients' physiological functions, facial aesthetics, and psychological well-being, posing significant challenges for precise reconstruction. Current deep learning models are limited to single-tissue scenarios and modality-specific imaging inputs, resulting in poor generalizability and trade-offs between anatomical fidelity, computational efficiency, and cross-tissue adaptability. Here we introduce UniDCF, a unified framework capable of reconstructing multiple dentocraniofacial hard tissues through multimodal fusion encoding of point clouds and multi-view images. By leveraging the complementary strengths of each modality and incorporating a score-based denoising module to refine surface smoothness, UniDCF overcomes the limitations of prior single-modality approaches. We curated the largest multimodal dataset, comprising intraoral scans, CBCT, and CT from 6,609 patients, resulting in 54,555 annotated instances. Evaluations demonstrate that UniDCF outperforms existing state-of-the-art methods in terms of geometric precision, structural completeness, and spatial accuracy. Clinical simulations indicate UniDCF reduces reconstruction design time by 99% and achieves clinician-rated acceptability exceeding 94%. Overall, UniDCF enables rapid, automated, and high-fidelity reconstruction, supporting personalized and precise restorative treatments, streamlining clinical workflows, and enhancing patient outcomes.

cs.CV↗

arXiv2025

Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties. However, the traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment. In this survey, we offer a systematic examination of innovative LLM architectures that address the inherent limitations of transformers and boost the efficiency. Starting from language modeling, this survey covers the background and technical details of linear and sparse sequence modeling methods, efficient full attention variants, sparse mixture-of-experts, hybrid model architectures incorporating the above techniques, and emerging diffusion LLMs. Additionally, we discuss applications of these techniques to other modalities and consider their wider implications for developing scalable, resource-aware foundation models. By grouping recent studies into the above category, this survey presents a blueprint of modern efficient LLM architectures, and we hope this could help motivate future research toward more efficient, versatile AI systems.

cs.CL↗

arXiv2025

An Empirical Study of CGO Usage in Go Projects -- Distribution, Purposes, Patterns and Critical Issues

Multilingual software development integrates multiple languages into a single application, with the Foreign Function Interface (FFI) enabling seamless interaction. While FFI boosts efficiency and extensibility, it also introduces risks. Existing studies focus on FFIs in languages like Python and Java, neglecting CGO, the emerging FFI in Go, which poses unique risks. To address these concerns, we conduct an empirical study of CGO usage across 920 open-source Go projects. Our study aims to reveal the distribution, patterns, purposes, and critical issues associated with CGO, offering insights for developers and the Go team. We develop CGOAnalyzer, a tool to efficiently identify and quantify CGO-related features. Our findings reveal that: (1) 11.3% of analyzed Go projects utilize CGO, with usage concentrated in a subset of projects; (2) CGO serves 4 primary purposes, including system-level interactions and performance optimizations, with 15 distinct usage patterns observed; (3) 19 types of CGO-related issues exist, including one critical issue involving unnecessary pointer checks that pose risks of runtime crashes due to limitations in the current Go compilation toolchain; (4) a temporary solution reduces unnecessary pointer checks, mitigating crash risks, and (5) we submitted a proposal to improve the Go toolchain for a permanent fix, which has been grouped within an accepted proposal for future resolution. Our findings provide valuable insights for developers and the Go team, enhancing development efficiency and reliability while improving the robustness of the Go toolchain.

cs.SE↗

arXiv2025

SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion

Most existing learning-based multi-modality image fusion (MMIF) methods suffer from significant structure inconsistency due to their inappropriate usage of structural features at the semantic level. To alleviate these issues, we propose a semantic structure-preserving fusion approach for MMIF, namely SSPFusion. At first, we design a structural feature extractor (SFE) to extract the prominent structural features from multiple input images. Concurrently, we introduce a transformation function with Sobel operator to generate self-supervised structural signals in these extracted features. Subsequently, we design a multi-scale structure-preserving fusion (SPF) module, guided by the generated structural signals, to merge the structural features of input images. This process ensures the preservation of semantic structure consistency between the resultant fusion image and the input images. Through the synergy of these two robust modules of SFE and SPF, our method can generate high-quality fusion images and demonstrate good generalization ability. Experimental results, on both infrared-visible image fusion and medical image fusion tasks, demonstrate that our method outperforms nine state-of-the-art methods in terms of both qualitative and quantitative evaluations. The code is publicly available at https://github.com/QiaoYang-CV/SSPFUSION.

cs.CV↗

arXiv2025

Think as Cardiac Sonographers: Marrying SAM with Left Ventricular Indicators Measurements According to Clinical Guidelines

Left ventricular (LV) indicator measurements following clinical echocardiog-raphy guidelines are important for diagnosing cardiovascular disease. Alt-hough existing algorithms have explored automated LV quantification, they can struggle to capture generic visual representations due to the normally small training datasets. Therefore, it is necessary to introduce vision founda-tional models (VFM) with abundant knowledge. However, VFMs represented by the segment anything model (SAM) are usually suitable for segmentation but incapable of identifying key anatomical points, which are critical in LV indicator measurements. In this paper, we propose a novel framework named AutoSAME, combining the powerful visual understanding of SAM with seg-mentation and landmark localization tasks simultaneously. Consequently, the framework mimics the operation of cardiac sonographers, achieving LV indi-cator measurements consistent with clinical guidelines. We further present fil-tered cross-branch attention (FCBA) in AutoSAME, which leverages relatively comprehensive features in the segmentation to enhance the heatmap regression (HR) of key points from the frequency domain perspective, optimizing the vis-ual representation learned by the latter. Moreover, we propose spatial-guided prompt alignment (SGPA) to automatically generate prompt embeddings guid-ed by spatial properties of LV, thereby improving the accuracy of dense pre-dictions by prior spatial knowledge. The extensive experiments on an echocar-diography dataset demonstrate the efficiency of each design and the superiori-ty of our AutoSAME in LV segmentation, landmark localization, and indicator measurements. The code will be available at https://github.com/QC-LIU-1997/AutoSAME.

cs.CV↗

arXiv2025

AgriGPT: a Large Language Model Ecosystem for Agriculture

Despite the rapid progress of Large Language Models (LLMs), their application in agriculture remains limited due to the lack of domain-specific models, curated datasets, and robust evaluation frameworks. To address these challenges, we propose AgriGPT, a domain-specialized LLM ecosystem for agricultural usage. At its core, we design a multi-agent scalable data engine that systematically compiles credible data sources into Agri-342K, a high-quality, standardized question-answer (QA) dataset. Trained on this dataset, AgriGPT supports a broad range of agricultural stakeholders, from practitioners to policy-makers. To enhance factual grounding, we employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning, thereby improving the LLM's reasoning reliability. For comprehensive evaluation, we introduce AgriBench-13K, a benchmark suite comprising 13 tasks with varying types and complexities. Experiments demonstrate that AgriGPT significantly outperforms general-purpose LLMs on both domain adaptation and reasoning. Beyond the model itself, AgriGPT represents a modular and extensible LLM ecosystem for agriculture, comprising structured data construction, retrieval-enhanced generation, and domain-specific evaluation. This work provides a generalizable framework for developing scientific and industry-specialized LLMs. All models, datasets, and code will be released to empower agricultural communities, especially in underserved regions, and to promote open, impactful research.

cs.AI↗

arXiv2025

Binary clusters in the Galactic disk I: Systematic identification and classification using Gaia DR3

Aims. We aim to identify and classify BCs using high-precision astrometric and kinematic data, and to investigate their physical properties, mutual gravitational interactions, and formation rates. Methods. We used a comprehensive star cluster catalog that contains 4,084 high-quality clusters. Based on spatial and kinematic proximity, we identified 400 cluster pairs involving 686 unique clusters. These pairs were classified into three types: primordial BCs, systems formed through tidal capture or resonant trapping, and hyperbolic encounter pairs. For each system, we calculated the tidal factor to quantify the strength of mutual tidal interaction. Additionally, we constructed multi-cluster systems by identifying transitive connections among cluster pairs. Results. Among the 400 identified cluster pairs, nearly 60.8% (243 pairs) are probably primordial BCs, exhibiting both similar ages and motions. This supports a scenario where they formed together in the same giant molecular cloud. We find that 82.5% of the cluster pairs have strong mutual tidal forces. In addition, 278 star clusters are identified as members of 82 multi-cluster systems, including 27 newly reported groups. Cross-matching with the literature confirms the recovery of previously reported systems and leads to the discovery of 268 new cluster pairs. In our sample, about 16.8% of star clusters are involved in some type of interaction with another cluster, and 9.94% of star clusters are likely born in primordial BCs. Conclusions. Our results provide a comprehensive, homogeneously identified sample of Galactic BCs. The high fraction of primordial BCs and their mutual tidal interaction suggest that cluster formation in pairs is a main outcome of star formation. This work offers new observational constraints on the formation and dynamical evolution of multiple star cluster systems.

astro-ph.GA↗

arXiv2025

Continuous-variable quantum key distribution over 50.4 km fiber using integrated silicon photonic transmitter and receiver

Quantum key distribution (QKD) is the fastest-growing and relatively mature technology in the field of quantum information, enabling information-theoretically secure key distribution between two remote users. Although QKD based on off-the-shelf telecom components has been validated in both laboratory and field tests, its high cost and large volume remain major obstacles to large-scale deployment. Photonic integration, featured by its compact size and low cost, offers an effective approach to addressing the above challenges faced by QKD. Here, we implement a high-performance, integrated local local oscillator continuous-variable (CV) QKD system based on an integrated silicon photonic transmitter and receiver. By employing a high-speed silicon photonic integrated in-phase and quadrature modulator, a low-noise and high bandwidth silicon photonic integrated heterodyne detector, and digital signal processing, our CV-QKD system achieves a symbol rate of up to 1.5625 GBaud. Furthermore, the system achieves asymptotic secret key rates of 31.05 and 5.05 Mbps over 25.8 and 50.4 km standard single-mode fiber, respectively, using an 8-phase-shift keying discrete modulation. Our integrated CV-QKD system with high symbol rate and long transmission distance pays the way for the quantum secure communication network at metropolitan area.

quant-ph↗

arXiv2025

Dark Count Rate Stability of JUNO 20-inch PMTs in Mass Testing

The Jiangmen Underground Neutrino Observatory (JUNO) is an ambitious multipurpose neutrino experiment designed to determine the neutrino mass ordering, with an impressive energy resolution goal of at least 3% at 1 MeV. To achieve a photon detection coverage of approximately 75%, JUNO will utilize two types of 20-inch photomultiplier tubes (PMTs): the large PMT (LPMT) and the microchannel plate PMT (MCP-PMT). A significant concern in high-precision neutrino measurements is the dark count rate (DCR) of PMTs, which introduces noise that can adversely affect energy measurement accuracy. During the mass testing phase of the JUNO 20-inch PMTs, comprehensive measurements of the DCR were undertaken. These measurements not only captured the DCR values of individual PMTs but also examined the stability and temperature dependence of the DCR at an operating gain of (1x10^7). This paper presents a detailed characterization of the DCR of the JUNO 20-inch PMTs, investigating factors such as cooling time, temperature variations, and long-term stability using the JUNO Pan-Asia PMT testing facilities. The results reveal distinct DCR characteristics between the two types of PMTs, providing valuable insights into the nature of DCR and its implications for JUNO's scientific objectives. In addition to performance characterization, we implemented a monitoring system to track DCR stability over time. Notably, several spikes in DCR were identified, prompting a preliminary investigation into their causes. Potential factors contributing to these spikes, such as flasher events, were explored using coincidence rate analysis and complementary imaging techniques. The findings from this study are crucial for optimizing the performance of PMTs in JUNO, ultimately aiding the experiment in achieving its goals related to neutrino physics.

physics.ins-det↗