arXiv subjects

Yiru Zhang

Publications and source records attributed to Yiru Zhang.

7 recordsLinked to original sources

arXiv2026

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD↗

arXiv2026

Periodic Orbits and Gravitational Radiation from Extreme Mass-Ratio Inspirals as Probes of Black Hole Quantum Hair

The classical no-hair theorem states that stationary black holes in general relativity can be completely described by only a small set of global parameters. Within this framework, no additional geometric structures are expected to persist outside the event horizon. However, quantum vacuum polarization may introduce small modifications to the near-horizon geometry, effectively giving rise to what is known as quantum hair. Such corrections may provide a possible window into the microscopic structure and thermodynamic properties of black holes. In this work, we examine how the quantum hair parameter {\gamma} influences the periodic orbital dynamics of test bodies in extreme mass-ratio inspirals (EMRIs) and their associated gravitational-wave emission. We find that {\gamma} significantly modifies the characteristic radii and angular momenta of two important circular orbits, namely the marginally bound orbit (MBO) and the innermost stable circular orbit (ISCO), leading to a shift in the allowed region of the energy-angular momentum (E-L) phase space. Based on the rational number q classification, we further show that quantum corrections tend to enhance the zoom-whirl orbital behavior.Gravitational-wave calculations using the Numerical Kludge approach indicate that quantum hair alters the effective spacetime potential, produces small drifts in the fundamental orbital frequencies, and consequently leads to observable phase dephasing in longduration signals. These results provide a dynamical signature for distinguishing quantum-corrected black holes from classical Schwarzschild ones and offer theoretical motivation for testing quantum gravity effects with future space-based gravitational-wave observatories.

gr-qc↗

arXiv2025

Exploration of Enterprise Big Data Microservice Architecture Based on Domain-Driven Design (DDD)

With the rapid advancement of digitization and intelligence, enterprise big data processing platforms have become increasingly important in data management. However, traditional monolithic architectures, due to their high coupling, are unable to cope with increasingly complex demands in the face of business expansion and increased data volume, resulting in limited platform scalability and decreased data collection efficiency. This article proposes a solution for enterprise big data processing platform based on microservice architecture, based on the concept of Domain Driven Design (DDD). Through in-depth analysis of business requirements, the functional and non functional requirements of the platform in various scenarios were determined, and the DDD method was used to decompose the core business logic into independent microservice modules, enabling data collection, parsing, cleaning, and visualization functions to be independently developed, deployed, and upgraded, thereby improving the flexibility and scalability of the system. This article also designs an automated data collection process based on microservices and proposes an improved dynamic scheduling algorithm to efficiently allocate data collection tasks to Docker nodes, and monitor the collection progress and service status in real time to ensure the accuracy and efficiency of data collection. Through the implementation and testing of the platform, it has been verified that the enterprise big data processing platform based on microservice architecture has significantly improved scalability, data quality, and collection efficiency.

cs.SI↗

arXiv2025

M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings

Portable physiological monitoring is essential for early detection and management of cardiovascular disease, but current methods often require specialized equipment that limits accessibility or impose impractical postures that patients cannot maintain. Video-based photoplethysmography on smartphones offers a convenient noninvasive alternative, yet it still faces reliability challenges caused by motion artifacts, lighting variations, and single-view constraints. Few studies have demonstrated reliable application to cardiovascular patients, and no widely used open datasets exist for cross-device accuracy. To address these limitations, we introduce the M3PD dataset, the first publicly available dual-view mobile photoplethysmography dataset, comprising synchronized facial and fingertip videos captured simultaneously via front and rear smartphone cameras from 60 participants (including 47 cardiovascular patients). Building on this dual-view setting, we further propose F3Mamba, which fuses the facial and fingertip views through Mamba-based temporal modeling. The model reduces heart-rate error by 21.9 to 30.2 percent over existing single-view baselines while improving robustness in challenging real-world scenarios. Data and code: https://github.com/Health-HCI-Group/F3Mamba.

cs.CV↗

arXiv2025

Thinking in cocktail party: Chain-of-Thought and reinforcement learning for target speaker automatic speech recognition

Target Speaker Automatic Speech Recognition (TS-ASR) aims to transcribe the speech of a specified target speaker from multi-speaker mixtures in cocktail party scenarios. Recent advancement of Large Audio-Language Models (LALMs) has already brought some new insights to TS-ASR. However, significant room for optimization remains for the TS-ASR task within the LALMs architecture. While Chain of Thoughts (CoT) and Reinforcement Learning (RL) have proven effective in certain speech tasks, TS-ASR, which requires the model to deeply comprehend speech signals, differentiate various speakers, and handle overlapping utterances is particularly well-suited to a reasoning-guided approach. Therefore, we propose a novel framework that incorporates CoT and RL training into TS-ASR for performance improvement. A novel CoT dataset of TS-ASR is constructed, and the TS-ASR model is first trained on regular data and then fine-tuned on CoT data. Finally, the model is further trained with RL using selected data to enhance generalized reasoning capabilities. Experiment results show a significant improvement of TS-ASR performance with CoT and RL training, which demonstrates the effectiveness of the proposed CoT and RL training methods adapted for the TS-ASR task.

cs.SD↗

arXiv2025

Contact Sensors to Remote Cameras: Quantifying Cardiorespiratory Coupling in High-Altitude Exercise Recovery

Cardiorespiratory coupling (CRC) captures the dynamic interaction between the cardiac and respiratory systems--an interaction strengthened by physical exercise and linked to improved physiological function. We examined CRC at high altitude in two states, rest and post-exercise recovery, and found significant differences (p < 0.05). Quantitative analysis revealed that recovery involved more frequent yet less stable episodes of synchronization between respiration and pulse. Furthermore, we explored the feasibility of non-contact CRC measurement with remote photoplethysmography (rPPG), observing a strong correlation with oximeter-based metrics (Pearson r = 0.96). These findings highlight the potential of CRC as a sensitive marker for autonomic regulation and its future application in contactless monitoring. Source code is available at GitHub: https://github.com/McJackTang/CRC.

cs.CE↗

arXiv2017

Preference fusion and Condorcet's Paradox under uncertainty

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.

cs.AI↗