arXiv subjects

Yicheng Zhong

Publications and source records attributed to Yicheng Zhong.

13 recordsLinked to original sources

arXiv2026

Pokrovsky--Talapov and Berezinskii--Kosterlitz--Thouless Phase Transitions in Bilayer Superconducting Films under an In-Plane Magnetic Field

We study finite-temperature phase transitions in a Josephson-coupled bilayer superconducting film with compact layer phases under an in-plane magnetic field. At zero temperature, where thermally excited layer vortices are absent, the relative-phase sector undergoes a Pokrovsky--Talapov (PT) commensurate--incommensurate (C--IC) transition from a commensurate Fulde--Ferrell (C/FF) state to an incommensurate Bloch superconducting (IC/Bloch SC) state. At finite temperature, compactness separates two distinct defect mechanisms. The C--IC boundary remains a PT soliton-entry line: interlayer Josephson vortex--antivortex-pair solitons enter with the square-root onset $ρ_{\rm sol}\propto [k_0-k_0^c(T)]^{1/2}$. Thermal melting is instead Berezinskii--Kosterlitz--Thouless (BKT)-like, with correlation exponent $η=1/4$ at the boundary, but the active vortex channel changes across the phase diagram. Josephson locking suppresses elementary layer vortices in the C/FF state and selects a same-vorticity layer-pair BKT channel, whereas elementary layer vortices control melting of the IC/Bloch SC state.

cond-mat.supr-con↗

arXiv2026

FineCombo-TTS: Collaborative and Precise Controllable Speech Synthesis Using Text Descriptions and Reference Speech

Controllable text-to-speech (TTS) has become a key research focus. However, methods based on either reference speech or text descriptions lack flexibility and precise control, and recent joint approaches remain loosely coupled, with speech modeling timbre and text controlling global style. We propose FineCombo-TTS, a unified framework for speech synthesis grounded in reference speech and guided by text descriptions, enabling flexible and precise control over acoustic attributes. Instead of explicit attribute disentanglement, we learn a unified acoustic representation and introduce a Conditional Flow Matching (CFM)-based Speech Variance Predictor to model fine-grained reference-to-target transformations guided by text descriptions. To support relative attribute control, we construct FineEdit, a structured paired dataset that explicitly encodes source-to-target attribute variations. Experiments demonstrate that our approach achieves flexible, precise, and expressive controllable TTS.

cs.SD↗

arXiv2026

TellWhisper: Tell Whisper Who Speaks When

Multi-speaker automatic speech recognition (MASR) aims to predict ''who spoke when and what'' from multi-speaker speech, a key technology for multi-party dialogue understanding. However, most existing approaches decouple temporal modeling and speaker modeling when addressing ''when'' and ''who'': some inject speaker cues before encoding (e.g., speaker masking), which can cause irreversible information loss; others fuse identity by mixing speaker posteriors after encoding, which may entangle acoustic content with speaker identity. This separation is brittle under rapid turn-taking and overlapping speech, often leading to degraded performance. To address these limitations, we propose TellWhisper, a unified framework that jointly models speaker identity and temporal within the speech encoder. Specifically, we design TS-RoPE, a time-speaker rotary positional encoding: time coordinates are derived from frame indices, while speaker coordinates are derived from speaker activity and pause cues. By applying region-specific rotation angles, the model explicitly captures per-speaker continuity, speaker-turn transitions, and state dynamics, enabling the attention mechanism to simultaneously attend to ''when'' and ''who''. Moreover, to estimate frame-level speaker activity, we develop Hyper-SD, which casts speaker classification in hyperbolic space to enhance inter-class separation and refine speaker-activity estimates. Extensive experiments demonstrate the effectiveness of the proposed approach.

eess.AS↗

arXiv2026

SimFuzz: Similarity-guided Block-level Mutation for RISC-V Processor Fuzzing

The Instruction Set Architecture (ISA) defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to processor design and encourages widespread adoption, but also exposes processors to security risks such as functional bugs. Processor fuzzing is a powerful technique for automatically detecting these bugs. However, existing fuzzing methods suffer from two main limitations. First, their emphasis on redundant test case generation causes them to overlook cross-processor corner cases. Second, they rely too heavily on coverage guidance. Current coverage metrics are biased and inefficient, and become ineffective once coverage growth plateaus. To overcome these limitations, we propose SimFuzz, a fuzzing framework that constructs a high-quality seed corpus from historical bug-triggering inputs and employs similarity-guided, block-level mutation to efficiently explore the processor input space. By introducing instruction similarity, SimFuzz expands the input space around seeds while preserving control-flow structure, enabling deeper exploration without relying on coverage feedback. We evaluate SimFuzz on three widely used open-source RISC-V processors: Rocket, BOOM, and XiangShan, and discover 17 bugs in total, including 14 previously unknown issues, 7 of which have been assigned CVE identifiers. These bugs affect the decode and memory units, cause instruction and data errors, and can lead to kernel instability or system crashes. Experimental results show that SimFuzz achieves up to 73.22% multiplexer coverage on the high-quality seed corpus. Our findings highlight critical security bugs in mainstream RISC-V processors and offer actionable insights for improving functional verification.

cs.CR↗

arXiv2025

Yamaji effect and quantum oscillation in Yang-Rice-Zhang model of underdoped cuprates

Recent experiments have revealed signatures of small Fermi pockets in the pseudogap phase of cuprate superconductors, most notably the Yamaji effect observed in $\mathrm{HgBa}_2\mathrm{CuO}_{4+δ}$. The Yang-Rice-Zhang (YRZ) model provides a successful phenomenological description of the pseudogap state and naturally predicts such small pockets. In this work, we use a microscopic framework to calculate angle-dependent magnetoresistance and quantum oscillation within the YRZ model. Our calculations simultaneously reproduce the experimentally observed Yamaji oscillations and the Shubnikov-de Haas oscillation corresponding to a pocket area of about $p/8$, with $p$ the hole density. By further testing the effect of Green's-function zeros, we confirm that isolated zeros leave the oscillation period unchanged, whereas an extended zero segment suppresses and modifies the oscillation. Our findings demonstrate that the YRZ model captures essential features of the pseudogap regime and provides a general quantum approach that can be applied to more complex electronic structures.

cond-mat.supr-con↗

arXiv2025

HPSU: A Benchmark for Human-Level Perception in Real-World Spoken Speech Understanding

Recent advances in Speech Large Language Models (Speech LLMs) have led to great progress in speech understanding tasks such as Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER). However, whether these models can achieve human-level auditory perception, particularly in terms of their ability to comprehend latent intentions and implicit emotions in real-world spoken language, remains underexplored. To this end, we introduce the Human-level Perception in Spoken Speech Understanding (HPSU), a new benchmark for fully evaluating the human-level perceptual and understanding capabilities of Speech LLMs. HPSU comprises over 20,000 expert-validated spoken language understanding samples in English and Chinese. It establishes a comprehensive evaluation framework by encompassing a spectrum of tasks, ranging from basic speaker attribute recognition to complex inference of latent intentions and implicit emotions. To address the issues of data scarcity and high cost of manual annotation in real-world scenarios, we developed a semi-automatic annotation process. This process fuses audio, textual, and visual information to enable precise speech understanding and labeling, thus enhancing both annotation efficiency and quality. We systematically evaluate various open-source and proprietary Speech LLMs. The results demonstrate that even top-performing models still fall considerably short of human capabilities in understanding genuine spoken interactions. Consequently, HPSU will be useful for guiding the development of Speech LLMs toward human-level perception and cognition.

cs.SD↗

arXiv2025

Multi-Reward GRPO for Stable and Prosodic Single-Codebook TTS LLMs at Scale

Recent advances in Large Language Models (LLMs) have transformed text-to-speech (TTS) synthesis, inspiring autoregressive frameworks that represent speech as sequences of discrete codec tokens. Among them, single-codebook TTS LLMs have emerged as compact and streamable architectures that jointly model semantic and acoustic integration. However, despite their efficiency, these models often exhibit unstable prosody, speaker drift, and degraded naturalness. To address these issues, we propose a multi-reward Group Relative Policy Optimization (GRPO) framework that directly optimizes the token generation policy of single-codebook TTS LLMs. Beyond standard intelligibility and speaker similarity objectives, our design integrates three rule-based rewards: a length penalty for duration consistency, an entropy regularization reward for decoding stability, and an LLM-annotated prosody alignment reward that explicitly supervises rhythm. In this prosody reward, an external reasoning LLM predicts multiple plausible pause structures via in-context learning, providing a human-preference-aligned supervisory signal for GRPO training. To assess universality, we further attach a flow-matching (FM) decoder on top of the GRPO-optimized AR backbone and observe consistent additional gains, indicating that our reinforcement optimization enhances the intrinsic AR policy. We further conduct a scalability analysis across data sizes and model scales, revealing that the proposed method consistently enhances prosodic stability, speaker similarity, and overall speech naturalness in single-codebook TTS LLMs.

cs.SD↗

arXiv2024

Optimizing Neural Speech Codec for Low-Bitrate Compression via Multi-Scale Encoding

Neural speech codecs have demonstrated their ability to compress high-quality speech and audio by converting them into discrete token representations. Most existing methods utilize Residual Vector Quantization (RVQ) to encode speech into multiple layers of discrete codes with uniform time scales. However, this strategy overlooks the differences in information density across various speech features, leading to redundant encoding of sparse information, which limits the performance of these methods at low bitrate. This paper proposes MsCodec, a novel multi-scale neural speech codec that encodes speech into multiple layers of discrete codes, each corresponding to a different time scale. This encourages the model to decouple speech features according to their diverse information densities, consequently enhancing the performance of speech compression. Furthermore, we incorporate mutual information loss to augment the diversity among speech codes across different layers. Experimental results indicate that our proposed method significantly improves codec performance at low bitrate.

cs.SD↗

arXiv2024

Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models

Spontaneous style speech synthesis, which aims to generate human-like speech, often encounters challenges due to the scarcity of high-quality data and limitations in model capabilities. Recent language model-based TTS systems can be trained on large, diverse, and low-quality speech datasets, resulting in highly natural synthesized speech. However, they are limited by the difficulty of simulating various spontaneous behaviors and capturing prosody variations in spontaneous speech. In this paper, we propose a novel spontaneous speech synthesis system based on language models. We systematically categorize and uniformly model diverse spontaneous behaviors. Moreover, fine-grained prosody modeling is introduced to enhance the model's ability to capture subtle prosody variations in spontaneous speech.Experimental results show that our proposed method significantly outperforms the baseline methods in terms of prosody naturalness and spontaneous behavior naturalness.

cs.SD↗

arXiv2023

ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment

The objective of stylized speech-driven facial animation is to create animations that encapsulate specific emotional expressions. Existing methods often depend on pre-established emotional labels or facial expression templates, which may limit the necessary flexibility for accurately conveying user intent. In this research, we introduce a technique that enables the control of arbitrary styles by leveraging natural language as emotion prompts. This technique presents benefits in terms of both flexibility and user-friendliness. To realize this objective, we initially construct a Text-Expression Alignment Dataset (TEAD), wherein each facial expression is paired with several prompt-like descriptions.We propose an innovative automatic annotation method, supported by Large Language Models (LLMs), to expedite the dataset construction, thereby eliminating the substantial expense of manual annotation. Following this, we utilize TEAD to train a CLIP-based model, termed ExpCLIP, which encodes text and facial expressions into semantically aligned style embeddings. The embeddings are subsequently integrated into the facial animation generator to yield expressive and controllable facial animations. Given the limited diversity of facial emotions in existing speech-driven facial animation training data, we further introduce an effective Expression Prompt Augmentation (EPA) mechanism to enable the animation generator to support unprecedented richness in style control. Comprehensive experiments illustrate that our method accomplishes expressive facial animation generation and offers enhanced flexibility in effectively conveying the desired style.

cs.CV↗

arXiv2000

Field-dependence of the Magnetic Relaxation in Mn12-Acetate: A New Form of Spectroscopy

We report point by point measurements below the blocking temperature of the magnetic relaxation of Mn$_{12}$-acetate as a function of magnetic field applied along the easy axis of magnetization. Unexpectedly complex structure is observed which we attribute to the effect of higher-order terms of the spin Hamiltonianon on the tunneling process.

cond-mat↗

arXiv1999

Low temperature magnetic hysteresis in Mn$_{12}$ acetate single crystals

Precise magnetic hysteresis measurements of small single crystals of Mn$_{12}$ acetate of spin 10 have been conducted down to 0.4 K using a high sensitivity Hall magnetometer. At higher temperature (>1.6K) step-like changes in magnetization are observed at regularly spaced magnetic field intervals, as previously reported. However, on lowering the temperature the steps in magnetization shift to higher magnetic fields, initially gradually. These results are consistent with the presence of a second order uniaxial magnetic anisotropy, first observed by EPR spectroscopy, and thermally assisted tunnelling with tunnelling relaxation occurring from levels of progressively lower energy as the temperature is reduced. At lower temperature an abrupt shift in step positions is found. We suggest that this shift may be the first evidence of an abrupt, or first-order, transition between thermally assisted and pure quantum tunnelling, suggested by recent theory.

cond-mat.mes-hall↗

arXiv1998

Inelastic Neutron Scattering Study of Mn_12-Acetate

We report zero-field inelastic neutron scattering experiments on a deuterated powder sample of Mn12-Acetate consisting of a large number of nominally identical spin-10 magnetic clusters. Our resolution enables us to see a series of peaks corresponding to transitions between the anisotropy levels within the spin-10 manifold. A fit to the spin Hamiltonian yields an anisotropy constant D=0.54 K and a fourth-order diagonal anisotropy coefficient A = 1.2x10^-3 K. Performed in the absence of a magnetic field, our experiments do not involve the g-values as fitting parameters, thereby yielding particularly reliable values of D and A.

cond-mat.mes-hall↗