arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 631 records · Page 35Linked to original sources

arXiv2025

UAKNN: Label Distribution Learning via Uncertainty-Aware KNN

Label Distribution Learning (LDL) aims to characterize the polysemy of an instance by building a set of descriptive degrees corresponding to the instance. In recent years, researchers seek to model to obtain an accurate label distribution by using low-rank, label relations, expert experiences, and label uncertainty estimation. In general, these methods are based on algorithms with parameter learning in a linear (including kernel functions) or deep learning framework. However, these methods are difficult to deploy and update online due to high training costs, limited scalability, and outlier sensitivity. To address this problem, we design a novel LDL method called UAKNN, which has the advantages of the KNN algorithm with the benefits of uncertainty modeling. In addition, we provide solutions to the dilemma of existing work on extremely label distribution spaces. Extensive experiments demonstrate that our method is significantly competitive on 12 benchmarks and that the inference speed of the model is well-suited for industrial-level applications.

cs.LG↗

arXiv2025

The Mini-SiTian Array: Imaging Processing Pipeline

As a pathfinder of the SiTian project, the Mini-SiTian (MST) array, employed three commercial CMOS cameras, represents a next-generation, cost-effective optical time-domain survey project. This paper focuses primarily on the precise data processing pipeline designed for wide-field, CMOS-based devices, including the removal of instrumental effects, astrometry, photometry, and flux calibration. When applying this pipeline to approximately 3000 observations taken in the Field 02 (f02) region by MST, the results demonstrate a remarkable astrometric precision of approximately 70--80\,mas (about 0.1\,pixel), an impressive calibration accuracy of approximately 1\,mmag in the MST zero points, and a photometric accuracy of about 4\,mmag for bright stars. Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs, highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys, like the SiTian project.

astro-ph.IM↗

arXiv2025

The Mini-SiTian Array: Evaluation Camera System

The Mini-SiTian project, which is the pathfinder for the SiTian project, utilizes three 30 cm telescopes equipped with commercial CMOS cameras (ZWO ASI6200MM Pro) to simulate large-area time-domain survey. Due to the avoidance of the traditional mechanical shutter, the CMOS camera is favorable in time-domain survey projects. In the future, the SiTian telescope array will employ a two-by-two scientific-grade mosaic CMOS camera to survey a 10,000-degree square area every 30 minutes. Therefore, the performance of CMOS directly determines the detection capability of SiTian telescopes for transient sources, and a comprehensive understanding of the performance of CMOS cameras is crucial. In this research, laboratory testing was conducted to thoroughly evaluate three cameras by assessing several critical parameters, including bias stability, dark current, pixel anomalies, linearity, gain, and read noise. We find exceptional short-term bias stability with standard deviations below 0.02 ADU, negligible dark current of approximately 0.002 e$^{-}$ pixel$^{-1}$ s$^{-1}$ at $0^\circ\text{C}$, and excellent linearity with nonlinearity consistently below $\pm$ 0.5\%, and a small proportion (0.06\% to 0.08\%) of pixels with anomalous responses. Furthermore, our analysis demonstrates uniform gain values across all cameras, ranging from 0.252 to 0.255 e$^{-}$ ADU$^{-1}$, with low readout noise, measured to be below 1.6 e$^{-}$ using conventional methods. We also propose a novel method for pixel-level gain and read noise calculation for CMOS sensors, which revealed a narrow gain distribution and a low median read noise of 1.028 e$^-$ for one of the cameras. The laboratory testing of the ZWO ASI6200MM Pro cameras indicates their potential to meet the requirements of time-domain surveys for the Mini-SiTian project.

astro-ph.IM↗

arXiv2025

The Mini-SiTian Array: first-two-year operation

The SiTian project, designed to utilize 60 telescopes distributed across multiple sites in China, is a next-generation time-domain survey initiative. As a pathfinder for the SiTian project, the Mini-SiTian (MST) has been proposed and implemented to test the SiTian's brain and data pipeline, and to evaluate the feasibility of its technology and science cases. Mounted at the Xinglong Observatory, the MST project comprises three 30 cm telescopes and has been operated since Nov. 2022. Each telescope of the MST possesses a large field of view, covering $2.29^{\circ}$ $\times$ $1.53^{\circ}$ FOV, and is equipped with $g'$, $r'$ and $i'$ filters, respectively. Acting as the pioneer of the forthcoming SiTian project, the MST is dedicated to the discovery of variable stars, transients, and outburst events, and has already obtained some interesting scientific results. In this paper, we will summarize the first-two-year operation of the MST project.

astro-ph.IM↗

arXiv2025

Transfer learning empowers material Z classification with muon tomography

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex muon event reconstruction and trajectory fitting processes. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of target materials, significantly limiting their practical applicability in detecting concealed materials. For the first time, transfer learning is introduced into the field of muon tomography in this work. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon tomography data of bare materials to predict the Z-class of coated materials. By employing the inverse cumulative distribution function method, more accurate scattering angle distributions could be obtained from limited data, leading to an improvement by nearly 4\% in prediction accuracy compared with the traditional random sampling based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieves an overall prediction accuracy exceeding 96\%, with high-Z materials reaching nearly 99\%. Simulation results indicate that transfer learning improves prediction accuracy by approximately 10\% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data, highlights the promising potential of transfer learning in the field of muon tomography.

physics.ins-det↗

arXiv2025

Reinforcement Learning for Active Matter

Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science.

cond-mat.soft↗

arXiv2025

MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis

While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined alignment-based models suffer from naturalness constraints of forced alignments. This paper introduces \textit{MegaTTS 3}, a TTS system featuring an innovative sparse alignment algorithm that guides the latent diffusion transformer (DiT). Specifically, we provide sparse alignment boundaries to MegaTTS 3 to reduce the difficulty of alignment without limiting the search space, thereby achieving high naturalness. Moreover, we employ a multi-condition classifier-free guidance strategy for accent intensity adjustment and adopt the piecewise rectified flow technique to accelerate the generation process. Experiments demonstrate that MegaTTS 3 achieves state-of-the-art zero-shot TTS speech quality and supports highly flexible control over accent intensity. Notably, our system can generate high-quality one-minute speech with only 8 sampling steps. Audio samples are available at https://sditdemo.github.io/sditdemo/.

eess.AS↗

arXiv2025

CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue

The unmanned aerial vehicle (UAV) network has gained significant attentions in recent years due to its various applications. However, the traffic security becomes the key threatening public safety issue in an emergency rescue system due to the increasing vulnerability of UAVs to cyber attacks in environments with high heterogeneities. Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology. Specifically, SDN separates the control and data plane to enhance the network manageability and security. Meanwhile, the blockchain provides decentralized identity authentication and data security records. Beisdes, a complete security architecture requires an effective mechanism to detect the time-series based abnormal traffic. Thus, an integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD. Finally, the simulation results show that the proposed CTranATD algorithm is effective and outperforms the individual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.

cs.LG↗

arXiv2025

UAV-Assisted MEC for Disaster Response: Stackelberg Game-Based Resource Optimization

The unmanned aerial vehicle assisted multi-access edge computing (UAV-MEC) technology has been widely applied in the sixth-generation era. However, due to the limitations of energy and computing resources in disaster areas, how to efficiently offload the tasks of damaged user equipments (UEs) to UAVs is a key issue. In this work, we consider a multiple UAVMECs assisted task offloading scenario, which is deployed inside the three-dimensional corridors and provide computation services for UEs. In detail, a ground UAV controller acts as the central decision-making unit for deploying the UAV-MECs and allocates the computational resources. Then, we model the relationship between the UAV controller and UEs based on the Stackelberg game. The problem is formulated to maximize the utility of both the UAV controller and UEs. To tackle the problem, we design a K-means based UAV localization and availability response mechanism to pre-deploy the UAV-MECs. Then, a chess-like particle swarm optimization probability based strategy selection learning optimization algorithm is proposed to deal with the resource allocation. Finally, extensive simulation results verify that the proposed scheme can significantly improve the utility of the UAV controller and UEs in various scenarios compared with baseline schemes.

eess.SP↗

arXiv2025

EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling

Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). This amplifies uncertainty and ambiguity in full-body motion estimation, especially for the lower-body joints. Therefore, we propose a new method, EnvPoser, that employs a two-stage framework to perform full-body motion estimation using sparse tracking signals and pre-scanned environment from VR devices. EnvPoser models the multi-hypothesis nature of human motion through an uncertainty-aware estimation module in the first stage. In the second stage, we refine these multi-hypothesis estimates by integrating semantic and geometric environmental constraints, ensuring that the final motion estimation aligns realistically with both the environmental context and physical interactions. Qualitative and quantitative experiments on two public datasets demonstrate that our method achieves state-of-the-art performance, highlighting significant improvements in human motion estimation within motion-environment interaction scenarios.

cs.CV↗

arXiv2025

EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions

GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging for the open-source community. Existing vision-language models rely on external tools for speech processing, while speech-language models still suffer from limited or totally without vision-understanding capabilities. To address this gap, we propose the EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech abilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we surprisingly notice that omni-modal alignment can further enhance vision-language and speech abilities compared with the bi-modal aligned counterparts. Moreover, a lightweight style module is introduced for the flexible speech style controls including emotions and pitches. For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.

cs.CV↗

arXiv2025

Formation and evolution of new primordial open cluster groups: Feedback-driven star formation

The formation mechanisms of open cluster (OCs) groups remain unclear due to limited sample sizes and data precision. Recent advancements in Gaia astrometric data provide an unprecedented opportunity to study OC groups in greater detail. This study aims to extend the sample of OC groups and investigate their formation and evolution mechanisms, with a focus on the role of stellar feedback in triggering star formation. We identify four new OC groups based on Gaia data, whose member OCs are spatially proximate and kinematically coherent. Their age spreads are consistent with the timescale of continuous star formation, suggesting that their member OCs formed sequentially from the same molecular cloud. N-body simulation results further reveal that these groups will gradually disperse, evolving into independent OCs. By analyzing the correlation between OC ages and their separation from potential SN explosion sites, we predict SN explosion regions around the birthplaces of OC groups. The strong correlation between OC ages and predicted SN explosion sites supports a supernova-triggered star formation scenario. Additionally, we trace pulsar (PSR) orbits to examine their association with these regions. We detected three PSRs near Group 1 and 26 PSRs near Group 2, whose birthplaces align with the predicted SN explosions regions. The presence of PSRs associated with OC groups provides additional observational evidence for SN explosions in this region, further supporting a supernova-triggered star formation scenario for G1 and G2. We propose that multiple SN explosions in a short period triggered the formation of Group 1 and Group 2, reinforcing the hierarchical star formation model. These results highlight the multi-scale interactions driving star and OC formation and provide new insights into the role of stellar feedback in shaping OC groups.

astro-ph.GA↗

arXiv2025

Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation

In the real world, users always have multiple interests while surfing different services to enrich their daily lives, e.g., watching hot short videos/live streamings. To describe user interests precisely for a better user experience, the recent literature proposes cross-domain techniques by transferring the other related services (a.k.a. domain) knowledge to enhance the accuracy of target service prediction. In practice, naive cross-domain techniques typically require there exist some overlapped users, and sharing overall information across domains, including user historical logs, user/item embeddings, and model parameter checkpoints. Nevertheless, other domain's user-side historical logs and embeddings are not always available in real-world RecSys designing, since users may be totally non-overlapped across domains, or the privacy-preserving policy limits the personalized information sharing across domains. Thereby, a challenging but valuable problem is raised: How to empower target domain prediction accuracy by utilizing the other domain model parameters checkpoints only? To answer the question, we propose the FMoE-CDSR, which explores the non-overlapped cross-domain sequential recommendation scenario from the federated learning perspective.

cs.IR↗

arXiv2025

Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction

The Transformer has proven to be a significant approach in feature interaction for CTR prediction, achieving considerable success in previous works. However, it also presents potential challenges in handling feature interactions. Firstly, Transformers may encounter information loss when capturing feature interactions. By relying on inner products to represent pairwise relationships, they compress raw interaction information, which can result in a degradation of fidelity. Secondly, due to the long-tail features distribution, feature fields with low information-abundance embeddings constrain the information abundance of other fields, leading to collapsed embedding matrices. To tackle these issues, we propose a Dual Attention Framework for Enhanced Feature Interaction, known as Dual Enhanced Attention. This framework integrates two attention mechanisms: the Combo-ID attention mechanism and the collapse-avoiding attention mechanism. The Combo-ID attention mechanism directly retains feature interaction pairs to mitigate information loss, while the collapse-avoiding attention mechanism adaptively filters out low information-abundance interaction pairs to prevent interaction collapse. Extensive experiments conducted on industrial datasets have shown the effectiveness of Dual Enhanced Attention.

cs.IR↗

arXiv2025

D2O: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models

Generative inference in Large Language Models (LLMs) is impeded by the growing memory demands of Key-Value (KV) cache, especially for longer sequences. Traditional KV cache eviction strategies, which discard less critical KV pairs based on attention scores, often degrade generation quality, leading to issues such as context loss or hallucinations. In this work, we introduce Dynamic Discriminative Operations (D2O), a KV cache compression method that optimizes KV cache size dynamically and discriminatively at two levels without fine-tuning, while preserving essential context. At layer level, D2O leverages the varying densities of attention weights between shallow and deep layers to dynamically determine which layers should avoid excessive eviction via a novel dynamic allocation strategy to minimize information loss. At token level, D2O incorporates a compensation mechanism that maintains a similarity threshold to re-discriminate the importance of currently discarded tokens, determining whether they should be recalled and merged with similar tokens. We conduct experiments on various benchmarks and LLM architectures. Our results show that D2O not only achieves significant memory savings and enhances inference throughput by more than 3$\times$ but also maintains high-quality long-text generation.

cs.CL↗

arXiv2025

Nonequilibrium mean-field approach for quantum transport with off-diagonal disorder

For the nanoscale structures, disorder scattering plays a vital role in the carriers' transport, including electrons and high-frequency phonons. The capability for effectively treating the disorders, including both diagonal and off-diagonal disorders, is indispensable for quantum transport simulation of realistic device materials. In this work, we report a self-consistent nonequilibrium mean-field quantum transport approach, by combining the auxiliary coherent potential approximation (ACPA) and non-equilibrium Green's function method, for calculating the phonon transport through disordered material structures with the force-constant disorders (including the Anderson-type disorder). The nonequilibrium vertex correction (NVC) is derived in an extended local degree of freedom to account for both the multiple disorder scattering by force-constant disorder and the nonequilibrium quantum statistics. We have tested ACPA-NVC method with the fluctuation-dissipation theorem at the equilibrium and obtained very good agreement with supercell calculations for the phonon transmission. To demonstrate the applicability, we apply ACPA-NVC to calculate the thermal conductance for the disordered Ni/Pt interface, and important effects of force-constant disorder are revealed. ACPA-NVC method provides an effective quantum transport approach for simulating disordered nanoscale devices, and the generalization to simulate disordered nanoelectronic device is straightforward.

cond-mat.mes-hall↗

arXiv2025

Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation

Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study, we establish a standardized dataset and evaluation framework for medical imaging QC, systematically assessing large language models (LLMs) in image quality assessment and report standardization. Specifically, we first constructed and anonymized a dataset of 161 chest X-ray (CXR) radiographs and 219 CT reports for evaluation. Then, multiple LLMs, including Gemini 2.0-Flash, GPT-4o, and DeepSeek-R1, were evaluated based on recall, precision, and F1 score to detect technical errors and inconsistencies. Experimental results show that Gemini 2.0-Flash achieved a Macro F1 score of 90 in CXR tasks, demonstrating strong generalization but limited fine-grained performance. DeepSeek-R1 excelled in CT report auditing with a 62.23\% recall rate, outperforming other models. However, its distilled variants performed poorly, while InternLM2.5-7B-chat exhibited the highest additional discovery rate, indicating broader but less precise error detection. These findings highlight the potential of LLMs in medical imaging QC, with DeepSeek-R1 and Gemini 2.0-Flash demonstrating superior performance.

cs.CL↗

arXiv2025

Sphere Precoding for Robust Near-Field Communications

Near-field communication with large antenna arrays promises significant beamforming and multiplexing gains. These communication links, however, are very sensitive to user mobility as any small change in the user position may suddenly drop the signal power. This leads to critical challenges for the robustness of these near-field communication systems. In this paper, we propose \textit{sphere precoding}, which is a robust precoding design to address user mobility in near-field communications. To gain insights into the spatial correlation of near-field channels, we extend the one-ring channel model to what we call one-sphere channel model and derive the channel covariance considering user mobility. Based on the one-sphere channel model, a robust precoding design problem is defined to optimize the minimum signal-to-interference-plus-noise ratio (SINR) satisfaction probability among mobile users. By utilizing the eigen structure of channel covariance, we further design a relaxed convex problem to approximate the solution of the original non-convex problem. The low-complexity solution effectively shapes a sphere that maintains the signal power for the target user and also nulls its interference within spheres around the other users. Simulation results highlight the efficacy of the proposed solution in achieving robust precoding yet high achievable rates in near-field communication systems.

eess.SP↗