arXiv subjects

Ming Cheng

Publications and source records attributed to Ming Cheng.

At least 37 records · Page 2Linked to original sources

arXiv2026

Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings

This paper proposes a Spatially-Augmented Sequence-to-Sequence Neural Diarization (SA-S2SND) framework, which integrates direction-of-arrival (DOA) cues estimated by SRP-DNN into the S2SND backbone. A two-stage training strategy is adopted: the model is first trained with single-channel audio and DOA features, and then further optimized with multi-channel inputs under DOA guidance. In addition, a simulated DOA generation scheme is introduced to alleviate dependence on matched multi-channel corpora. On the AliMeeting dataset, SA-S2SND consistently outperform the S2SND baseline, achieving a 7.4% relative DER reduction in the offline mode and over 19% improvement when combined with channel attention. These results demonstrate that spatial cues are highly complementary to cross-channel modeling, yielding good performance in both online and offline settings.

eess.AS↗

arXiv2025

Design and Research of a Self-Propelled Pipeline Robot Based on Force Analysis and Dynamic Simulation

In pipeline inspection, traditional tethered inspection robots are severely constrained by cable length and weight, which greatly limit their travel range and accessibility. To address these issues, this paper proposes a self-propelled pipeline robot design based on force analysis and dynamic simulation, with a specific focus on solving core challenges including vertical climbing failure and poor passability in T-branch pipes. Adopting a wheeled configuration and modular design, the robot prioritizes the core demand of body motion control. Specifically, 3D modeling of the robot was first completed using SolidWorks. Subsequently, the model was imported into ADAMS for dynamic simulation, which provided a basis for optimizing the drive module and motion control strategy.To verify the robot's dynamic performance, an experimental platform with acrylic pipes was constructed. Through adjusting its body posture to surmount obstacles and select directions, the robot has demonstrated its ability to stably traverse various complex pipeline scenarios. Notably, this work offers a technical feasibility reference for the application of pipeline robots in the inspection of medium and low-pressure urban gas pipelines.

cs.RO↗

arXiv2025

Ultrafast μeV-Precision Bandgap Engineering in Low-Dimensional Topological Insulators

Precise and ultrafast control of electronic band structures is a central challenge for advancing quantum functional materials and devices. Conventional approaches--such as chemical doping, lattice strain, or external gating--offer robust stability but remain confined to the quasi-static regime, far from the intrinsic femto- to picosecond dynamics that govern many-body interactions. Here, using cryogenic transient reflectance spectroscopy, we realize dynamic bandgap engineering in the anisotropic topological insulator $α$-Bi$_4$Br$_4$ with unprecedented micro-electron-volt ($μ$eV) precision. The exceptional sensitivity arises from the cooperative action of long-lived topological carriers, stabilized by restricted bulk-to-edge scattering phase space, together with symmetry-resolved coherent phonons that modulate inter-chain hopping. These channels jointly modify Coulomb screening and interband transitions, enabling both gradual and oscillatory control of the electronic structure. Supported by first-principles and tight-binding theory, we further demonstrate a dual-pump coherent control strategy for continuous, mode-selective tuning of electronic energies with $μ$eV accuracy. This framework paves the way for ultrafast on-demand band-structure engineering, pointing toward new frontiers in quantum optoelectronics, precision measurement in molecular and biological systems, and attosecond control of matter.

cond-mat.mes-hall↗

arXiv2025

Anisotropy of linear magnetoresistance in Kagome metal ZrV$_6$Sn$_6$

The Kagome lattice has attracted extensive attention due to the diverse magnetic properties and non-trivial electronic states generated by its unique atomic arrangement, which provides an excellent system for exploring macroscopic quantum behavior. Here, we report the anomalous transport properties in 166-type Kagome metal ZrV$_6$Sn$_6$ single crystals. The quadratic and linear magnetoresistance (LMR) can be observed depending on the directions of the field and the current. Integrating Hall resistivity and quantum oscillation measurements, we found that the LMR could match well with the Abrikosov model. However, this model encounters difficulties in explaining the anisotropy of the magnetoresistance. To solve the issue, we extrapolate the Abrikosov model to the case of two-dimensional linear dispersion. It was found that when the field is parallel to the linear dependence momentum, the quantized energy is $ε_n^{\pm}$ = $\pm v\sqrt{p^2+2eHn/c}$, resulting in LMR. By contrast, when it is parallel to the non-linear dependence momentum, the energy is $ε_n^{\pm}$ = $\pm v\sqrt{2eHn/c}$, without yielding LMR. Through the combination of experiment and theory, the modified Abrikosov model could interpret the macroscopic quantum transport in ZrV$_6$Sn$_6$ crystal. The present research provides a new perspective for understanding the LMR behavior.

cond-mat.str-el↗

arXiv2025

A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling

Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been limited by reliance on human annotation, which is costly, inconsistent, difficult to scale. Their associated datasets are often limited to specific topics and strategies, posing challenges for robust model development. We propose a novel framework that leverages large language models (LLMs) to automatically generate and label synthetic debate data based on a four-part rhetorical typology (causal, empirical, emotional, moral). We fine-tune transformer-based classifiers on this LLM-labeled dataset and validate its performance against human-labeled data on this dataset and on multiple external corpora. Our model achieves high performance and strong generalization across topical domains. We illustrate two applications with the fine-tuned model: (1) the improvement in persuasiveness prediction from incorporating rhetorical strategy labels, and (2) analyzing temporal and partisan shifts in rhetorical strategies in U.S. Presidential debates (1960-2020), revealing increased use of affective over cognitive argument in U.S. Presidential debates.

cs.CL↗

arXiv2025

ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering

Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous systems, where models must provide not only accurate answers but also explanations that humans can easily understand and verify. Prototype-based modeling has shown promise for interpretability by grounding predictions in semantically meaningful regions for purely visual reasoning tasks, yet remains underexplored in the context of VQA. We present ProtoVQA, a unified prototypical framework that (i) learns question-aware prototypes that serve as reasoning anchors, connecting answers to discriminative image regions, (ii) applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant, and (iii) supports both answering and grounding tasks through a shared prototype backbone. To assess explanation quality, we propose the Visual-Linguistic Alignment Score (VLAS), which measures how well the model's attended regions align with ground-truth evidence. Experiments on Visual7W show that ProtoVQA yields faithful, fine-grained explanations while maintaining competitive accuracy, advancing the development of transparent and trustworthy VQA systems.

cs.CV↗

arXiv2025

MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation

Recent advancements in large language models (LLMs) have greatly improved code generation, specifically at the function level. For instance, GPT-4o has achieved a 91.0\% pass rate on HumanEval. However, this draws into question the adequacy of existing benchmarks in thoroughly assessing function-level code generation capabilities. Our study analyzed two common benchmarks, HumanEval and MBPP, and found that these might not thoroughly evaluate LLMs' code generation capacities due to limitations in quality, difficulty, and granularity. To resolve this, we introduce the Mostly Hard Python Problems (MHPP) dataset, consisting of 210 unique human-curated problems. By focusing on the combination of natural language and code reasoning, MHPP gauges LLMs' abilities to comprehend specifications and restrictions, engage in multi-step reasoning, and apply coding knowledge effectively. Initial evaluations of 26 LLMs using MHPP showed many high-performing models on HumanEval failed to achieve similar success on MHPP. Moreover, MHPP highlighted various previously undiscovered limitations within various LLMs, leading us to believe that it could pave the way for a better understanding of LLMs' capabilities and limitations. MHPP, evaluation pipeline, and leaderboard can be found in https://github.com/SparksofAGI/MHPP.

cs.CL↗

arXiv2025

VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models

Attribute Value Extraction (AVE) is important for structuring product information in e-commerce. However, existing AVE datasets are primarily limited to text-to-text or image-to-text settings, lacking support for product videos, diverse attribute coverage, and public availability. To address these gaps, we introduce VideoAVE, the first publicly available video-to-text e-commerce AVE dataset across 14 different domains and covering 172 unique attributes. To ensure data quality, we propose a post-hoc CLIP-based Mixture of Experts filtering system (CLIP-MoE) to remove the mismatched video-product pairs, resulting in a refined dataset of 224k training data and 25k evaluation data. In order to evaluate the usability of the dataset, we further establish a comprehensive benchmark by evaluating several state-of-the-art video vision language models (VLMs) under both attribute-conditioned value prediction and open attribute-value pair extraction tasks. Our results analysis reveals that video-to-text AVE remains a challenging problem, particularly in open settings, and there is still room for developing more advanced VLMs capable of leveraging effective temporal information. The dataset and benchmark code for VideoAVE are available at: https://github.com/gjiaying/VideoAVE

cs.CV↗

arXiv2025

Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization

Audio-visual learning has demonstrated promising results in many classical speech tasks (e.g., speech separation, automatic speech recognition, wake-word spotting). We believe that introducing visual modality will also benefit speaker diarization. To date, Target-Speaker Voice Activity Detection (TS-VAD) plays an important role in highly accurate speaker diarization. However, previous TS-VAD models take audio features and utilize the speaker's acoustic footprint to distinguish his or her personal speech activities, which is easily affected by overlapped speech in multi-speaker scenarios. Although visual information naturally tolerates overlapped speech, it suffers from spatial occlusion, low resolution, etc. The potential modality-missing problem blocks TS-VAD towards an audio-visual approach. This paper proposes a novel Multi-Input Multi-Output Target-Speaker Voice Activity Detection (MIMO-TSVAD) framework for speaker diarization. The proposed method can take audio-visual input and leverage the speaker's acoustic footprint or lip track to flexibly conduct audio-based, video-based, and audio-visual speaker diarization in a unified sequence-to-sequence framework. Experimental results show that the MIMO-TSVAD framework demonstrates state-of-the-art performance on the VoxConverse, DIHARD-III, and MISP 2022 datasets under corresponding evaluation metrics, obtaining the Diarization Error Rates (DERs) of 4.18%, 10.10%, and 8.15%, respectively. In addition, it can perform robustly in heavy lip-missing scenarios.

eess.AS↗

arXiv2025

The DKU System for Multi-Speaker Automatic Speech Recognition in MLC-SLM Challenge

We present the DKU system for Task 2 of the MLC-SLM Challenge, which aims to perform multi-speaker automatic speech recognition directly from raw audio without Oracle speaker labels or time boundaries. Our approach builds upon a diarization-aware framework integrating speaker embeddings and temporal utterance boundaries into a Qwen2.5-based large language model (LLM). Then, we enhance the system's multilingual performance by fine-tuning language-specific adapters and LoRA modules within the LLM decoder. Finally, our system achieves the tcpWER of 23.56\% and 18.08\% on the development and test sets of the MLC-SLM dataset, substantially outperforming the official baseline.

eess.AS↗

arXiv2025

Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation

This paper proposes a novel Sequence-to-Sequence Neural Diarization (S2SND) framework to perform online and offline speaker diarization. It is developed from the sequence-to-sequence architecture of our previous target-speaker voice activity detection system and then evolves into a new diarization paradigm by addressing two critical problems. 1) Speaker Detection: The proposed approach can utilize partially given speaker embeddings to discover the unknown speaker and predict the target voice activities in the audio signal. It does not require a prior diarization system for speaker enrollment in advance. 2) Speaker Representation: The proposed approach can adopt the predicted voice activities as reference information to extract speaker embeddings from the audio signal simultaneously. The representation space of speaker embedding is jointly learned within the whole diarization network without using an extra speaker embedding model. During inference, the S2SND framework can process long audio recordings blockwise. The detection module utilizes the previously obtained speaker-embedding buffer to predict both enrolled and unknown speakers' voice activities for each coming audio block. Next, the speaker-embedding buffer is updated according to the predictions of the representation module. Assuming that up to one new speaker may appear in a small block shift, our model iteratively predicts the results of each block and extracts target embeddings for the subsequent blocks until the signal ends. Finally, the last speaker-embedding buffer can re-score the entire audio, achieving highly accurate diarization performance as an offline system. Experimental results show that ...

eess.AS↗

arXiv2025

Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models

Multi-speaker automatic speech recognition (MS-ASR) faces significant challenges in transcribing overlapped speech, a task critical for applications like meeting transcription and conversational analysis. While serialized output training (SOT)-style methods serve as common solutions, they often discard absolute timing information, limiting their utility in time-sensitive scenarios. Leveraging recent advances in large language models (LLMs) for conversational audio processing, we propose a novel diarization-aware multi-speaker ASR system that integrates speaker diarization with LLM-based transcription. Our framework processes structured diarization inputs alongside frame-level speaker and semantic embeddings, enabling the LLM to generate segment-level transcriptions. Experiments demonstrate that the system achieves robust performance in multilingual dyadic conversations and excels in complex, high-overlap multi-speaker meeting scenarios. This work highlights the potential of LLMs as unified back-ends for joint speaker-aware segmentation and transcription.

eess.AS↗

arXiv2025

Learning Sparsity for Effective and Efficient Music Performance Question Answering

Music performances, characterized by dense and continuous audio as well as seamless audio-visual integration, present unique challenges for multimodal scene understanding and reasoning. Recent Music Performance Audio-Visual Question Answering (Music AVQA) datasets have been proposed to reflect these challenges, highlighting the continued need for more effective integration of audio-visual representations in complex question answering. However, existing Music AVQA methods often rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, the reduction of redundancy, and the prioritization of critical samples. To address these challenges, we introduce Sparsify, a sparse learning framework specifically designed for Music AVQA. It integrates three sparsification strategies into an end-to-end pipeline and achieves state-of-the-art performance on the Music AVQA datasets. In addition, it reduces training time by 28.32% compared to its fully trained dense counterpart while maintaining accuracy, demonstrating clear efficiency gains. To further improve data efficiency, we propose a key-subset selection algorithm that selects and uses approximately 25% of MUSIC-AVQA v2.0 training data and retains 70-80% of full-data performance across models.

cs.SD↗

arXiv2025

Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing

Lay paraphrasing aims to make scientific information accessible to audiences without technical backgrounds. However, most existing studies focus on a single domain, such as biomedicine. With the rise of interdisciplinary research, it is increasingly necessary to comprehend knowledge spanning multiple technical fields. To address this, we propose Sci-LoRA, a model that leverages a mixture of LoRAs fine-tuned on multiple scientific domains. In particular, Sci-LoRA dynamically generates and applies weights for each LoRA, enabling it to adjust the impact of different domains based on the input text, without requiring explicit domain labels. To balance domain-specific knowledge and generalization across various domains, Sci-LoRA integrates information at both the data and model levels. This dynamic fusion enhances the adaptability and performance across various domains. Experimental results across twelve domains on five public datasets show that Sci-LoRA significantly outperforms state-of-the-art large language models and demonstrates flexible generalization and adaptability in cross-domain lay paraphrasing.

cs.CL↗

arXiv2025

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

This paper describes the speaker diarization system developed for the Multimodal Information-Based Speech Processing (MISP) 2025 Challenge. First, we utilize the Sequence-to-Sequence Neural Diarization (S2SND) framework to generate initial predictions using single-channel audio. Then, we extend the original S2SND framework to create a new version, Multi-Channel Sequence-to-Sequence Neural Diarization (MC-S2SND), which refines the initial results using multi-channel audio. The final system achieves a diarization error rate (DER) of 8.09% on the evaluation set of the competition database, ranking first place in the speaker diarization task of the MISP 2025 Challenge.

eess.AS↗

arXiv2025

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks

Stochastic Discount Factor (SDF) models provide a unified framework for asset pricing and risk assessment, yet traditional formulations struggle to incorporate unstructured textual information. We introduce NewsNet-SDF, a novel deep learning framework that seamlessly integrates pretrained language model embeddings with financial time series through adversarial networks. Our multimodal architecture processes financial news using GTE-multilingual models, extracts temporal patterns from macroeconomic data via LSTM networks, and normalizes firm characteristics, fusing these heterogeneous information sources through an innovative adversarial training mechanism. Our dataset encompasses approximately 2.5 million news articles and 10,000 unique securities, addressing the computational challenges of processing and aligning text data with financial time series. Empirical evaluations on U.S. equity data (1980-2022) demonstrate NewsNet-SDF substantially outperforms alternatives with a Sharpe ratio of 2.80. The model shows a 471% improvement over CAPM, over 200% improvement versus traditional SDF implementations, and a 74% reduction in pricing errors compared to the Fama-French five-factor model. In comprehensive comparisons, our deep learning approach consistently outperforms traditional, modern, and other neural asset pricing models across all key metrics. Ablation studies confirm that text embeddings contribute significantly more to model performance than macroeconomic features, with news-derived principal components ranking among the most influential determinants of SDF dynamics. These results validate the effectiveness of our multimodal deep learning approach in integrating unstructured text with traditional financial data for more accurate asset pricing, providing new insights for digital intelligent decision-making in financial technology.

q-fin.PM↗

arXiv2025

Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis

This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training data (often inaccessible during online inference) and are limited to recognizing a fixed set of point cloud classes predefined during training, we explore a more practical and challenging scenario: adapting the model solely based on online test data to recognize both previously seen classes and novel, unseen classes at test time. To this end, we develop \textbf{Point-Cache}, a hierarchical cache model that captures essential clues of online test samples, particularly focusing on the global structure of point clouds and their local-part details. Point-Cache, which serves as a rich 3D knowledge base, is dynamically managed to prioritize the inclusion of high-quality samples. Designed as a plug-and-play module, our method can be flexibly integrated into large multimodal 3D models to support open-vocabulary point cloud recognition. Notably, our solution operates with efficiency comparable to zero-shot inference, as it is entirely training-free. Point-Cache demonstrates substantial gains across 8 challenging benchmarks and 4 representative large 3D models, highlighting its effectiveness. Code is available at https://github.com/auniquesun/Point-Cache.

cs.CV↗

arXiv2025

LACE: Exploring Turn-Taking and Parallel Interaction Modes in Human-AI Co-Creation for Iterative Image Generation

This paper introduces LACE, a co-creative system enabling professional artists to leverage generative AI through controlled prompting and iterative refinement within Photoshop. Addressing challenges in precision, iterative coherence, and workflow compatibility, LACE allows flexible control via layer-based editing and dual-mode collaboration (turn-taking and parallel). A pilot study (N=21) demonstrates significant improvements in user satisfaction, ownership, usability, and artistic perception compared to standard AI workflows. We offer comprehensive findings, system details, nuanced user feedback, and implications for integrating generative AI in professional art practices.

cs.HC↗