arXiv subjects

Qian Chen

Publications and source records attributed to Qian Chen.

At least 109 records · Page 6Linked to original sources

arXiv2026

SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models

The Speaker Diarization and Recognition (SDR) task aims to predict "who spoke when and what" within an audio clip, which is a crucial task in various real-world multi-speaker scenarios such as meeting transcription and dialogue systems. Existing SDR systems typically adopt a cascaded framework, combining multiple modules such as speaker diarization (SD) and automatic speech recognition (ASR). The cascaded systems suffer from several limitations, such as error propagation, difficulty in handling overlapping speech, and lack of joint optimization for exploring the synergy between SD and ASR tasks. To address these limitations, we introduce SpeakerLM, a unified multimodal large language model for SDR that jointly performs SD and ASR in an end-to-end manner. Moreover, to facilitate diverse real-world scenarios, we incorporate a flexible speaker registration mechanism into SpeakerLM, enabling SDR under different speaker registration settings. SpeakerLM is progressively developed with a multi-stage training strategy on large-scale real data. Extensive experiments show that SpeakerLM demonstrates strong data scaling capability and generalizability, outperforming state-of-the-art cascaded baselines on both in-domain and out-of-domain public SDR benchmarks. Furthermore, experimental results show that the proposed speaker registration mechanism effectively ensures robust SDR performance of SpeakerLM across diverse speaker registration conditions and varying numbers of registered speakers.

cs.SD↗

arXiv2025

FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks

To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission of SGINs is to support federated learning (FL) at a global scale. However, existing space-ground integrated FL frameworks involve ground stations or costly inter-satellite links, entailing excessive training latency and communication costs. To overcome these limitations, we propose an infrastructure-free federated learning framework based on a model dispersal (FedMeld) strategy, which exploits periodic movement patterns and store-carry-forward capabilities of satellites to enable parameter mixing across large-scale geographical regions. We theoretically show that FedMeld leads to global model convergence and quantify the effects of round interval and mixing ratio between adjacent areas on its learning performance. Based on the theoretical results, we formulate a joint optimization problem to design the staleness control and mixing ratio (SC-MR) for minimizing the training loss. By decomposing the problem into sequential SC and MR subproblems without compromising the optimality, we derive the round interval solution in a closed form and the mixing ratio in a semi-closed form to achieve the optimal latency-accuracy tradeoff. Experiments using various datasets demonstrate that FedMeld achieves superior model accuracy while significantly reducing communication costs as compared with traditional FL schemes for SGINs.

cs.LG↗

arXiv2025

DrVoice: Parallel Speech-Text Voice Conversation Model via Dual-Resolution Speech Representations

Recent studies on end-to-end (E2E) speech generation with large language models (LLMs) have attracted significant community attention, with multiple works extending text-based LLMs to generate discrete speech tokens. Existing E2E approaches primarily fall into two categories: (1) Methods that generate discrete speech tokens independently without incorporating them into the LLM's autoregressive process, resulting in text generation being unaware of concurrent speech synthesis. (2) Models that generate interleaved or parallel speech-text tokens through joint autoregressive modeling, enabling mutual modality awareness during generation. This paper presents DrVoice, a parallel speech-text voice conversation model based on joint autoregressive modeling, featuring dual-resolution speech representations. Notably, while current methods utilize mainly 12.5Hz input audio representation, our proposed dual-resolution mechanism reduces the input frequency for the LLM to 5Hz, significantly reducing computational cost and alleviating the frequency discrepancy between speech and text tokens and in turn better exploiting LLMs' capabilities. Experimental results demonstrate that DrVoice-7B establishes new state-of-the-art (SOTA) on prominent speech benchmarks including OpenAudioBench, VoiceBench, UltraEval-Audio and Big Bench Audio, making it a leading open-source speech foundation model in ~7B models.

cs.CL↗

arXiv2025

IGR J17091-3624: Newly Formed Periodic Dips and Multiwavelength Activity During the 2025 Outburst

The black hole low-mass X-ray binary (LMXB) candidate IGR J17091-3624 experienced a hard-state-only outburst in 2025. In this paper, we show that IXPE detected a series of intermittent X-ray dips, spanning a total interval of ~1 day. Subsequent observations with NICER, EP, NuSTAR, and Swift reveal that these dips recur with a period of 2.83$\pm$0.07 days and are accompanied by an increase in spectral hardness. This is the first time such quasi-periodic dipping behavior has been observed in this target since discovery. Our spectral analysis shows that the dips can be explained by obscuration from an ionized absorber characterized by an ionization parameter of $logξ$ ~1-3 erg cm s$^{-1}$ and an equivalent hydrogen column density of $N^{\rm zxipcf}_{\rm H}$~(1-30)$\times10^{22}$ cm$^{-2}$. The periodic reappearance of the absorber is likely caused by obscuring material located in the outer accretion disk, modulated by the binary orbital period. If confirmed, this period would suggest that the donor star in IGR J17091-3624 has deviated from the standard main-sequence evolutionary path and is likely a (partially) stripped giant. In the optical band, no significant periodicity or correlation with the X-ray dips was detected, whereas the radio counterpart exhibited a flat to steep spectrum, in contrast to the inverted spectrum typically observed during the hard state of LMXBs.

astro-ph.HE↗

arXiv2025

Crossover Dynamics of Non-Fickian Ionic Diffusion in Solids

Ionic diffusion in solids is central to energy storage, electronics, and catalysis, yet its chemical origins are difficult to resolve because conventional diffusion models struggle with effects of confinement, crystallographic disorder, lattice distortions, and coupling to electronic or phononic carriers. These challenges are especially pronounced in battery materials, where ionic and electronic motion occur together, complicating interpretation of electrochemical measurements. Here we use tracer exchange as a direct, non-electrochemical probe to reveal distinct ion-transport regimes in the one-dimensional conductor olivine Li_xFePO4 (0 <= x <= 1). Lithium isotope exchange validates single-file diffusion governed by strong ion-ion correlations, where 1D confinement suppresses bypassing and preserves spatial order. Kinetic Monte Carlo simulations and chronoamperometry quantify both Faradaic and non-Faradaic surface exchange, identifying electron transport, rather than Li+ mobility, as the rate-limiting step for electrochemical reaction. In addition, Li-Na exchange exhibits apparent superdiffusion, with rates that increase with Na content. Simulations attribute this behavior to surface-exchange limitations and Na-induced lattice strain that enhances cross-channel Li+ hopping and drives a crossover from 1D to quasi-2D transport. Four-dimensional STEM, in situ synchrotron XRD, X-ray absorption spectroscopy, and Mossbauer spectroscopy confirm that lattice softening and concerted polaron motion contribute to the observed dynamics. These results establish tracer exchange as a powerful tool for probing coupled ion-electron transport and provide chemical insight into how lattice mechanics and multicomponent exchange shape ionic diffusion in solids.

cond-mat.mtrl-sci↗

arXiv2025

On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks

Convex quadratically constrained quadratic programs (QCQPs) involve finding a solution within a convex feasible region defined by quadratic constraints while minimizing a convex quadratic objective function. These problems arise in various industrial applications, including power systems and signal processing. Traditional methods for solving convex QCQPs primarily rely on matrix factorization, which quickly becomes computationally prohibitive as the problem size increases. Recently, graph neural networks (GNNs) have gained attention for their potential in representing and solving various optimization problems such as linear programs and linearly constrained quadratic programs. In this work, we investigate the representation power of GNNs in the context of QCQP tasks. Specifically, we propose a new tripartite graph representation for general convex QCQPs and properly associate it with message-passing GNNs. We demonstrate that there exist GNNs capable of reliably representing key properties of convex QCQPs, including feasibility, optimal value, and optimal solution. Our result deepens the understanding of the connection between QCQPs and GNNs, paving the way for future machine learning approaches to efficiently solve QCQPs.

math.OC↗

arXiv2025

Synthetic Spatiotemporal Plasmonic Vortices On Chip

Spatiotemporal vortices are polychromatic modes that intertwine orbital angular momentum (OAM) in space and time. Here we introduce a new class of such vortices, spatiotemporal plasmonic vortices (STPVs), carrying nontrivial topological spin textures. They are generated by chronotopic interference of temporally delayed plasmonic eigen-vortices, where a $π$-phase dislocation in the space-frequency domain maps into a 2$π$ spiraling phase in space-time, with the resulting focus-defocus dynamics emulate U(1) gauge transitions. Using interferometric time-resolved photoemission electron microscopy (ITR-PEEM), we directly image their nanometer-attosecond (nano-atto) evolution and control vortex number and position. Quantum-path analysis of coherent two-photon photoemission (2PP) processes reveals the nonlinear plasmonic polarization fields and angular-momentum conservation, establishing STPVs as a platform for probing spatiotemporally structured quantum matter.

cond-mat.mes-hall↗

arXiv2025

Say More with Less: Variable-Frame-Rate Speech Tokenization via Adaptive Clustering and Implicit Duration Coding

Existing speech tokenizers typically assign a fixed number of tokens per second, regardless of the varying information density or temporal fluctuations in the speech signal. This uniform token allocation mismatches the intrinsic structure of speech, where information is distributed unevenly over time. To address this, we propose VARSTok, a VAriable-frame-Rate Speech Tokenizer that adapts token allocation based on local feature similarity. VARSTok introduces two key innovations: (1) a temporal-aware density peak clustering algorithm that adaptively segments speech into variable-length units, and (2) a novel implicit duration coding scheme that embeds both content and temporal span into a single token index, eliminating the need for auxiliary duration predictors. Extensive experiments show that VARSTok significantly outperforms strong fixed-rate baselines. Notably, it achieves superior reconstruction naturalness while using up to 23% fewer tokens than a 40 Hz fixed-frame-rate baseline. VARSTok further yields lower word error rates and improved naturalness in zero-shot text-to-speech synthesis. To the best of our knowledge, this is the first work to demonstrate that a fully dynamic, variable-frame-rate acoustic speech tokenizer can be seamlessly integrated into downstream speech language models.

eess.AS↗

arXiv2025

DiffusionUavLoc: Visually Prompted Diffusion for Cross-View UAV Localization

With the rapid growth of the low-altitude economy, unmanned aerial vehicles (UAVs) have become key platforms for measurement and tracking in intelligent patrol systems. However, in GNSS-denied environments, localization schemes that rely solely on satellite signals are prone to failure. Cross-view image retrieval-based localization is a promising alternative, yet substantial geometric and appearance domain gaps exist between oblique UAV views and nadir satellite orthophotos. Moreover, conventional approaches often depend on complex network architectures, text prompts, or large amounts of annotation, which hinders generalization. To address these issues, we propose DiffusionUavLoc, a cross-view localization framework that is image-prompted, text-free, diffusion-centric, and employs a VAE for unified representation. We first use training-free geometric rendering to synthesize pseudo-satellite images from UAV imagery as structural prompts. We then design a text-free conditional diffusion model that fuses multimodal structural cues to learn features robust to viewpoint changes. At inference, descriptors are computed at a fixed time step t and compared using cosine similarity. On University-1652 and SUES-200, the method performs competitively for cross-view localization, especially for satellite-to-drone in University-1652.Our data and code will be published at the following URL: https://github.com/liutao23/DiffusionUavLoc.git.

cs.CV↗

arXiv2025

MEDIC: Zero-shot Music Editing with Disentangled Inversion Control

Text-guided diffusion models revolutionize audio generation by adapting source audio to specific text prompts. However, existing zero-shot audio editing methods such as DDIM inversion accumulate errors across diffusion steps, reducing the effectiveness. Moreover, existing editing methods struggle with conducting complex non-rigid music edits while maintaining content integrity and high fidelity. To address these challenges, we propose MEDIC, a novel zero-shot music editing system based on innovative Disentangled Inversion Control (DIC) technique, which comprises Harmonized Attention Control and Disentangled Inversion. Disentangled Inversion disentangles the diffusion process into triple branches to rectify the deviated path of the source branch caused by DDIM inversion. Harmonized Attention Control unifies the mutual self-attention control and the cross-attention control with an intermediate Harmonic Branch to progressively generate the desired harmonic and melodic information in the target music. We also introduce ZoME-Bench, a comprehensive music editing benchmark with 1,100 samples covering ten distinct editing categories. ZoME-Bench facilitates both zero-shot and instruction-based music editing tasks. Our method outperforms state-of-the-art inversion techniques in editing fidelity and content preservation. The code and benchmark will be released. Audio samples are available at https://medic-edit.github.io/.

eess.AS↗

arXiv2025

ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing

While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging. Like professionals in the creative industries, this generation requires sophisticated reasoning about items such as visual dynamics, acoustic environments, and temporal relationships. We present ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos. Our approach decomposes the process into three complementary stages: foundational foley generation that creates semantically coherent soundscapes, interactive object-centric refinement through precise user interactions, and targeted editing guided by natural language instructions. At each stage, a multimodal large language model generates contextually aligned CoT reasoning that guides a unified audio foundation model. Furthermore, we introduce AudioCoT, a comprehensive dataset with structured reasoning annotations that establishes connections between visual content, textual descriptions, and sound synthesis. Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics, and excels in the out-of-distribution Movie Gen Audio benchmark. The project page is available at https://ThinkSound-Project.github.io.

eess.AS↗

arXiv2025

Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation

Image Auto-regressive (AR) models have emerged as a powerful paradigm of visual generative models. Despite their promising performance, they suffer from slow generation speed due to the large number of sampling steps required. Although Distilled Decoding 1 (DD1) was recently proposed to enable few-step sampling for image AR models, it still incurs significant performance degradation in the one-step setting, and relies on a pre-defined mapping that limits its flexibility. In this work, we propose a new method, Distilled Decoding 2 (DD2), to further advances the feasibility of one-step sampling for image AR models. Unlike DD1, DD2 does not without rely on a pre-defined mapping. We view the original AR model as a teacher model which provides the ground truth conditional score in the latent embedding space at each token position. Based on this, we propose a novel \emph{conditional score distillation loss} to train a one-step generator. Specifically, we train a separate network to predict the conditional score of the generated distribution and apply score distillation at every token position conditioned on previous tokens. Experimental results show that DD2 enables one-step sampling for image AR models with an minimal FID increase from 3.40 to 5.43 on ImageNet-256. Compared to the strongest baseline DD1, DD2 reduces the gap between the one-step sampling and original AR model by 67%, with up to 12.3$\times$ training speed-up simultaneously. DD2 takes a significant step toward the goal of one-step AR generation, opening up new possibilities for fast and high-quality AR modeling. Code is available at https://github.com/imagination-research/Distilled-Decoding-2.

cs.LG↗

arXiv2025

PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference

By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at network edge. However, how to reduce the inference latency remains a significant challenge. To address this issue, we develop a parameter-sharing AI model loading (PartialLoading) framework for multi-user edge inference, which exploits two key insights: 1) the majority of latency arises from loading AI models into server GPU memory, and 2) different AI models can share a significant number of parameters, for which redundant loading should be avoided. Towards this end, we formulate a joint multi-user scheduling and spectrum bandwidth allocation problem to maximize task throughput by exploiting shared parameter blocks across models. The intuition is to judiciously schedule user requests to reuse the shared parameter blocks between consecutively loaded models, thereby reducing model loading time substantially. To facilitate solution finding, we decouple the problem into two sub-problems, i.e., user scheduling and bandwidth allocation, showing that solving them sequentially leads to the solution to the original problem. Due to the NP-hardness of the problem, we first study an important special case called the "backbone-sharing" case, and design a dynamic programming-based algorithm to obtain the optimal solution in polynomial time. For the general case, we propose a greedy heuristic to obtain the sub-optimal solution efficiently. Simulation results demonstrate that the proposed framework significantly improves task throughput under deadline constraints compared with user scheduling without exploiting parameter sharing.

cs.NI↗

arXiv2025

Spin-supersolidity induced quantum criticality and magnetocaloric effect in the triangular-lattice antiferromagnet Rb$_2$Co(SeO$_3$)$_2$

We performed high-field magnetization, magnetocaloric effect (MCE), and NMR measurements on the Ising triangular-lattice antiferromagnet Rb$_2$Co(SeO$_3$)$_2$. The observations of the 1/3-magnetization plateau, the split NMR lines, and the thermal activation behaviors of the spin-lattice relaxation rate $1/T_1$ between 2 T and 15.8 T provide unambiguous evidence of a gapped up-up-down (UUD) magnetic ordered phase. For fields between 15.8 T and 18.5 T, the anomaly in the magnetic susceptibility, the slow saturation of the NMR line spectral ratio with temperature, and the power-law temperature dependence of $1/T_1$ suggest the ground state to be a spin supersolid with gapless spin excitations. With further increasing the field, the Grüneisen ratio, extracted from the MCE data, reveals a continuous quantum phase transition at $H_{\rm C}\approx$ 19.5 T and a universal quantum critical scaling with the exponents $νz~\approx~$1. Near $H_{\rm C}$, the large high-temperature MCE signal and the broad peaks in the NMR Knight shift and $1/T_1$, manifest the strong spin fluctuations driven by both magnetic frustration and quantum criticality. These results establish Rb$_2$Co(SeO$_3$)$_2$ as a candidate platform for cryogenic magnetocaloric cooling.

cond-mat.str-el↗

arXiv2025

QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks

The combination of linear transformations and non-linear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly complex functions. This paper explores the introduction of quadratic transformations to further increase nonlinearity in neural networks, with the aim of enhancing the performance of existing architectures. To reduce parameter complexity and computational complexity, we propose a lightweight quadratic enhancer that uses low-rankness, weight sharing, and sparsification techniques. For a fixed architecture, the proposed approach introduces quadratic interactions between features at every layer, while only adding negligible amounts of additional model parameters and forward computations. We conduct a set of proof-of-concept experiments for the proposed method across three tasks: image classification, text classification, and fine-tuning large-language models. In all tasks, the proposed approach demonstrates clear and substantial performance gains.

cs.LG↗

arXiv2025

Pretabular Tense Logics over S4t

A logic $L$ is called tabular if it is the logic of some finite frame and $L$ is pretabular if it is not tabular while all of its proper consistent extensions are tabular. In this work, we study pretabular tense logics in the lattice $\mathsf{NExt}(\mathsf{S4}_t)$ of all extensions of $\mathsf{S4}_t$, tense $\mathsf{S4}$. For all $0<n,m,k,l\leq\aleph_0$, we define the tense logic $\mathsf{S4BP}_{n,m}^{k,l}$ with respectively bounded width, depth and z-degree. We give a full characterization of the set $\mathsf{PTAB}(\mathsf{S4.3}_t)$ of all pretabular logics extending $\mathsf{S4.3}_t$, which entails that there are exactly 5 pretabular logics in $\mathsf{NExt}(\mathsf{S4.3}_t)$. Moreover, by providing a full characterization of $\mathsf{PTAB}(\mathsf{S4BP}^{2,ω}_{2,2})$ and proving that $|\mathsf{PTAB}(\mathsf{S4BP}^{2,ω}_{2,3})|=2^{\aleph_0}$, we show the anti-dichotomy theorem for cardinality of pretabular extensions in $\mathsf{NExt}(\mathsf{S4}_t)$: for all cardinal $κ$ such that $κ\leq{\aleph_0}$ or $κ=2^{\aleph_0}$, $|\mathsf{PTAB}(L)|=κ$ for some $L\in\mathsf{NExt}(\mathsf{S4}_t)$. It follows that $|\mathsf{PTAB}(\mathsf{S4}_t)|=2^{\aleph_0}$, which answers an open problem concerning the cardinality of $\mathsf{PTAB}(\mathsf{S4}_t)$ raised by Rautenberg in 1979.

math.LO↗

arXiv2025

Degree of Kripke-incompleteness of Tense Logics

The degree of Kripke-incompleteness of a logic $L$ in some lattice $\mathcal{L}$ of logics is the cardinality of logics in $\mathcal{L}$ which share the same class of Kripke-frames with $L$. A celebrated result on Kripke-incompleteness is Blok's dichotomy theorem for the degree of Kripke-incompleteness in $\mathsf{NExt}(\mathsf{K})$: every modal logic $L\in\mathsf{NExt}(\mathsf{K})$ is of the degree of Kripke-incompleteness $1$ or $2^{\aleph_0}$. In this work, we show that the dichotomy theorem for $\mathsf{NExt}(\mathsf{K})$ can be generalized to the lattices $\K$, $\LT$ and $\NExt(\ST)$ of tense logics. We also prove that in $\K$, $\LT$ and $\NExt(\ST)$, iterated splittings are exactly the strictly Kripke-complete logics.

math.LO↗

arXiv2025

Stability analysis for $Q$-balls with spectral method

Based on the spectral decomposition technique, we introduce a simple and universal numerical method to analyze the stability of solitons. Adopting this method, the linear dynamical properties of $Q$-balls are systematically revealed, from the fundamental to the excited states. For the fundamental $Q$-ball, the well-known stability criterion holds. However, for the excited $Q$-balls, the situation becomes extremely complicated, in which the stability criterion is violated. The system exhibits dynamical instability to both spherically symmetric and non-spherically symmetric perturbations, manifested in the appearance of complex and imaginary modes. In addition, we observe two interesting phenomena. One is that the oscillation mode and the complex or imaginary mode can transform into each other, marking the transition of the dynamical properties of the system. The other is the existence of excited $Q$-balls capable of resisting perturbations with low-order spherical harmonics. Such results indicate that the excited $Q$-balls will exhibit rich dynamical behaviors.

hep-th↗