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Yong Xu

Publications and source records attributed to Yong Xu.

At least 55 records · Page 3Linked to original sources
arXiv2026

Probing Bose-enhanced Inflaton Decay with Gravitational Waves

We investigate cosmic reheating dynamics in the presence of a transient condensate formed by bosonic decay products of the inflaton. We show that the emergence of such a condensate and the corresponding Bose enhancement can dramatically increase the efficiency of inflaton decay, giving rise to qualitatively new reheating dynamics beyond the standard perturbative picture. As a consequence, graviton production from inflaton decay processes is significantly amplified by Bose enhancement effects, leading to a stochastic gravitational-wave background with a potentially observable amplitude, even in the low-frequency regime.

hep-ph↗
arXiv2026

SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models

Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses despite safety alignment. Existing defenses typically improve safety through external filtering, auxiliary guardrails, or decoding-time control. However, these interventions often reduce practical deployability because they may require additional model access, introduce extra inference cost, or affect benign-task utility. In this paper, we propose Safety-Aware Intent Defense (SAID), a training-free jailbreak defense framework based on intent-level safety probing. SAID first distills potentially obfuscated user inputs into concise core intents using the target model itself. It then applies a validated safety prefix to probe each distilled intent and elicit the model's safety-aware response. Finally, a conservative aggregation rule rejects the original request if any distilled intent is identified as unsafe. This design enables black-box-compatible defense without updating model parameters or modifying the decoding process. Experiments on four open-source LLMs under six representative jailbreak attacks show that SAID achieves state-of-the-art defense performance in reducing harmful responses while maintaining competitive utility on benign tasks. Further analyses on prefix variants, hierarchical distillation, and inference efficiency demonstrate that SAID provides a practical safety-utility trade-off for securing LLMs against jailbreak threats.

cs.CR↗
arXiv2026

Feature-Label Modal Alignment for Robust Partial Multi-Label Learning

In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrupts the correspondence between features and labels, degrading classification performance. To address this challenge, we propose a novel PML method based on feature-label modal alignment (PML-MA), which treats features and labels as two complementary modalities and restores their consistency through systematic alignment. Specifically, PML-MA first employs low-rank orthogonal decomposition to generate pseudo-labels that approximate the true label distribution by filtering noisy labels. It then aligns features and pseudo-labels through both global projection into a common subspace and local preservation of neighborhood structures. Finally, a multi-peak class prototype learning mechanism leverages the multi-label nature where instances simultaneously belong to multiple categories, using pseudo-labels as soft membership weights to enhance discriminability. By integrating modal alignment with prototype-guided refinement, PML-MA ensures pseudo-labels better reflect the true distribution while maintaining robustness against label noise. Extensive experiments on both real-world and synthetic datasets demonstrate that PML-MA significantly outperforms state-of-the-art methods, achieving superior classification accuracy and noise robustness.

cs.LG↗
arXiv2026

Chiral Spin Liquid in Rydberg Atom Arrays

Despite long-standing theoretical interest, the chiral spin liquid, a topologically ordered phase, has yet to be observed experimentally. Here we surprisingly find its emergence in an experimentally realized dipolar $\text{XY}$ model when Rydberg atoms are arranged in a breathing kagome lattice. Using the infinite density matrix renormalization group, we numerically calculate the ground state's chiral order parameter, spin-spin correlations, Chern number, and entanglement spectrum. Our numerical results provide strong evidence for the chiral spin liquid phase. Furthermore, we identify a quantum phase transition from a Dirac spin liquid to a chiral spin liquid as the lattice geometry is tuned from the isotropic kagome to the breathing kagome lattice.

cond-mat.str-el↗
arXiv2026

PhysNeXt: Next-Generation Dual-Branch Structured Attention Fusion Network for Remote Photoplethysmography Measurement

Remote photoplethysmography (rPPG) enables contactless measurement of heart rate and other vital signs by analyzing subtle color variations in facial skin induced by cardiac pulsation. Current rPPG methods are mainly based on either end-to-end modeling from raw videos or intermediate spatial-temporal map (STMap) representations. The former preserves complete spatiotemporal information and can capture subtle heartbeat-related signals, but it also introduces substantial noise from motion artifacts and illumination variations. The latter stacks the temporal color changes of multiple facial regions of interest into compact two-dimensional representations, significantly reducing data volume and computational complexity, although some high-frequency details may be lost. To effectively integrate the mutual strengths, we propose PhysNeXt, a dual-input deep learning framework that jointly exploits video frames and STMap representations. By incorporating a spatio-temporal difference modeling unit, a cross-modal interaction module, and a structured attention-based decoder, PhysNeXt collaboratively enhances the robustness of pulse signal extraction. Experimental results demonstrate that PhysNeXt achieves more stable and fine-grained rPPG signal recovery under challenging conditions, validating the effectiveness of joint modeling of video and STMap representations. The codes will be released.

cs.CV↗
arXiv2026

ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models

Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating textual rationales for forgery detection. However, processing dense visual sequences incurs high computational costs, particularly for high-resolution images and videos. Visual token pruning is a practical acceleration strategy, yet existing methods are largely semantic-driven, retaining salient objects while discarding background regions where manipulation traces such as high-frequency anomalies and temporal jitters often reside. To address this issue, we introduce ForensicZip, a training-free framework that reformulates token compression from a forgery-driven perspective. ForensicZip models temporal token evolution as a Birth-Death Optimal Transport problem with a slack dummy node, quantifying physical discontinuities indicating transient generative artifacts. The forensic scoring further integrates transport-based novelty with high-frequency priors to separate forensic evidence from semantic content under large-ratio compression. Experiments on deepfake and AIGC benchmarks show that at 10\% token retention, ForensicZip achieves $2.97\times$ speedup and over 90\% FLOPs reduction while maintaining state-of-the-art detection performance.

cs.CV↗
arXiv2026

PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement

Remote photoplethysmography (rPPG) measurement enables non-contact physiological monitoring but suffers from accuracy degradation under head motion and illumination changes. Existing deep learning methods are mostly heuristic and lack theoretical grounding, limiting robustness and interpretability. In this work, we propose a physics-informed rPPG paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order dynamical system whose discrete solution naturally leads to a causal convolution, justifying the use of a Temporal Convolutional Network (TCN). Based on this principle, we design the PHASE-Net, a lightweight model with three key components: 1) Zero-FLOPs Axial Swapper module to swap or transpose a few spatial channels to mix distant facial regions, boosting cross-region feature interaction without changing temporal order; 2) Adaptive Spatial Filter to learn a soft spatial mask per frame to highlight signal-rich areas and suppress noise for cleaner feature maps; and 3) Gated TCN, a causal dilated TCN with gating that models long-range temporal dynamics for accurate pulse recovery. Extensive experiments demonstrate that PHASE-Net achieves state-of-the-art performance and strong efficiency, offering a theoretically grounded and deployment-ready rPPG solution. The source code is available at https://github.com/Alex036225/PhaseNet.

cs.CV↗
arXiv2026

Stochastic resonance in higher-order networks driven by colored noise

We investigate stochastic resonance (SR) in an ensemble of coupled overdamped bistable oscillators driven by colored noise. The networks incorporate the weighted contributions of both pairwise coupling and 2-simplex coupling. Our findings show that these higher-order interactions further exacerbate the suppression effect of colored noise on SR, reducing the peak of resonance curves and shifting the optimal noise intensity toward higher values. To clarify the underlying mechanism, we establish a close connection between SR and the four-stage variation in network synchronization level. Specially, the synchronization extremes explain the effect of higher-order coupling and colored noise on SR. Our analysis reveals that higher-order interactions do not reverse, but primarily promote the spatial propagation of suppression effects due to colored noise.

cond-mat.stat-mech↗
arXiv2026

Manipulating Charge Distribution in Moiré Superlattices by Light

In ordinary solids, nonlinear optical responses are typically studied in terms of unit-cell averages due to the angström-scale lattice constants. In contrast, moiré superlattices, characterized by a large length scale, unlock an often-overlooked degree of freedom: intra-supercell spatial variations of local observables. Here, we formulate the second-order direct current (DC) charge response in a spatially resolved manner, showing that even uniform optical illumination can drive a static, spatially non-uniform charge redistribution within a supercell. This effect is ubiquitous and cannot be forbidden by any crystalline symmetries. Furthermore, we identify a dominant contribution arising from diverging analytical response coefficients, which leads to linear-in-time growth of the redistribution in the absence of relaxation. This growth is driven by the convergence or divergence of local DC photocurrents. Applying our theory to twisted bilayer MoTe$_2$, we demonstrate strong, highly tunable charge modulation controlled by light intensity and frequency, opening a route to in situ, all-optical control of moiré-periodic electrostatic potentials. Our work underscores the importance of intra-cell degrees of freedom, which enable a qualitatively richer class of nonlinear optical responses in moiré superlattices.

cond-mat.mtrl-sci↗
arXiv2026

Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide, making early and accurate DR grading critical for timely intervention. Recent clinical practices leverage multi-view fundus images for DR detection with a wide coverage of the field of view (FOV), motivating deep learning methods to explore the potential of multi-view learning for DR grading. However, existing methods often overlook the inter-view correlations when fusing multi-view fundus images, failing to fully exploit the inherent consistency across views originating from the same patient. In this work, we present MVGFDR, an end-to-end Multi-View Graph Fusion framework for DR grading. Different from existing methods that directly fuse visual features from multiple views, MVGFDR is equipped with a novel Multi-View Graph Fusion (MVGF) module to explicitly disentangle the shared and view-specific visual features. Specifically, MVGF comprises three key components: (1) Multi-view Graph Initialization, which constructs visual graphs via residual-guided connections and employs Discrete Cosine Transform (DCT) coefficients as frequency-domain anchors; (2) Multi-view Graph Fusion, which integrates selective nodes across multi-view graphs based on frequency-domain relevance to capture complementary view-specific information; and (3) Masked Cross-view Reconstruction, which leverages masked reconstruction of shared information across views to facilitate view-invariant representation learning. Extensive experimental results on MFIDDR, by far the largest multi-view fundus image dataset, demonstrate the superiority of our proposed approach over existing state-of-the-art approaches in diabetic retinopathy grading.

cs.CV↗
arXiv2026

Fermion-to-Fermion Low-Density Parity-Check Codes

Simulating fermionic systems on qubit-based quantum computers often demands significant computational resources due to the requirement to map fermions to qubits. Thus, designing a fault-tolerant quantum computer that operates directly with fermions offers an effective solution to this challenge. Here, we introduce a protocol for fault-tolerant fermionic quantum computation utilizing fermion-to-fermion low-density parity-check (LDPC) codes. Our method employs a fermionic LDPC memory, which transfers its state to fermionic color code processors, where logical operations are subsequently performed. We propose using odd-weight logical Majorana operators to form the code space, serving as memory for the fermionic LDPC code, and provide an algorithm to identify these logical operators. We present examples showing that the encoding rate of fermionic codes often matches that of qubit codes, while the logical failure rate can be significantly lower than the physical error rate. Furthermore, we propose two methods for performing fermionic lattice surgery to facilitate state transfer. Finally, we simulate the dynamics of a fermionic system using our protocol, illustrating effective error suppression.

quant-ph↗
arXiv2026

Self-dual Stacked Quantum Low-Density Parity-Check Codes

Quantum low-density parity-check (qLDPC) codes are promising candidates for fault-tolerant quantum computation due to their high encoding rates and distances. However, implementing logical operations using qLDPC codes presents significant challenges. Previous research has demonstrated that self-dual qLDPC codes facilitate the implementation of transversal Clifford gates. Here we introduce a method for constructing self-dual qLDPC codes by stacking non-self-dual qLDPC codes. Leveraging this methodology, we develop double-chain bicycle codes, double-layer bivariate bicycle (BB) codes, double-layer twisted BB codes, and double-layer reflection codes, many of which exhibit favorable code parameters. Additionally, we conduct numerical calculations to assess the performance of these codes as quantum memory under the circuit-level noise model, revealing that the logical failure rate can be significantly reduced with high pseudo-thresholds.

quant-ph↗
arXiv2026

Multiple Layer-Selective Polar Charge Density Waves in ${\rm{EuTe}}_{4}$

${\rm{EuTe}}_{4}$ is a polar charge density wave (CDW) material, with giant thermal hysteresis and non-volatile state switching under electric and optical fields, attracting great attention in recent years. However, the in-depth understanding of these anomalous phenomena remains elusive. Herein, via first-principles calculations, we reveal that the polar CDW state in ${\rm{EuTe}}_{4}$ hosts a novel layer-selective nature, wherein multiple energetically close CDW configurations coexist and exhibit low interconversion energy barriers. Monte Carlo simulations indicate that the giant thermal hysteresis in ${\rm{EuTe}}_{4}$ originates from a phase transition mainly driven by the change of configurational entropy, around which the material hosts a metastable CDW state characterized by diverse local polar configurations breaking the out-of-plane translational symmetry. The configurational composition of this metastable CDW state can be effectively controlled by electric and optical fields, thereby enabling non-volatile state switching. Our theoretical findings align well with recent experimental observations in ${\rm{EuTe}}_{4}$ and pave the way for exploring the emerging phenomena and applications of polar CDW in multilayered systems.

cond-mat.mtrl-sci↗
arXiv2026

SpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone Arrays

Spatial information is a critical clue for multi-channel multi-speaker target speech recognition. Most state-of-the-art multi-channel Automatic Speech Recognition (ASR) systems extract spatial features only during the speech separation stage, followed by standard single-channel ASR on the separated speech. This approach results in an inefficient, lengthy pipeline and sub-optimal ASR performance due to the accumulated errors from preprocessing modules. Furthermore, most spatial feature extraction methods depend on the knowledge of speaker positions and microphone topology, making the systems reliant on specific settings and challenging to adapt to new equipment. In this work, we propose a solution to these issues with a lightweight embedding module named SpatialEmb, which extracts and encodes spatial information directly for the ASR model, supporting both fixed and arbitrary microphone topology. We conduct comprehensive experiments on AliMeeting, a real meeting corpus, to determine the optimal model design for SpatialEmb in terms of both performance and efficiency. Our best model trained with 105 hours Train-Ali-far achieves 17.04% and 20.32% character error rates (CER) on the Eval and Test sets, establishing a new state-of-the-art result with the same training data.

eess.AS↗
arXiv2026

Accurate Calibration and Robust LiDAR-Inertial Odometry for Spinning Actuated LiDAR Systems

Accurate calibration and robust localization are fundamental for downstream tasks in spinning actuated LiDAR applications. Existing methods, however, require parameterizing extrinsic parameters based on different mounting configurations, limiting their generalizability. Additionally, spinning actuated LiDAR inevitably scans featureless regions, which complicates the balance between scanning coverage and localization robustness. To address these challenges, this letter presents a targetless LiDAR-motor calibration (LM-Calibr) on the basis of the Denavit-Hartenberg convention and an environmental adaptive LiDAR-inertial odometry (EVA-LIO). LM-Calibr supports calibration of LiDAR-motor systems with various mounting configurations. Extensive experiments demonstrate its accuracy and convergence across different scenarios, mounting angles, and initial values. Additionally, EVA-LIO adaptively selects downsample rates and map resolutions according to spatial scale. This adaptivity enables the actuator to operate at maximum speed, thereby enhancing scanning completeness while ensuring robust localization, even when LiDAR briefly scans featureless areas. The source code and hardware design are available on GitHub: \textcolor{blue}{\href{https://github.com/zijiechenrobotics/lm_calibr}{github.com/zijiechenrobotics/lm\_calibr}}. The video is available at \textcolor{blue}{\href{https://youtu.be/cZyyrkmeoSk}{youtu.be/cZyyrkmeoSk}}

cs.RO↗
arXiv2026

A Wide-Sense Stationarity Test Based on the Geometric Structure of Covariance

This paper presents a test for wide-sense stationarity (WSS) based on the geometry of the covariance function. We estimate local patches of the covariance surface and then check whether the directional derivative in the $(1,1,0)$ direction is zero on each patch. The method only requires the covariance function to be locally smooth and does not assume stationarity in advance. It can be applied to general stochastic dynamical systems and provides a time-resolved view. We apply the test method to an SDOF system and to a stochastic Duffing oscillator. These examples show that the method is numerically stable and can detect departures from WSS in practice.

stat.ME↗
arXiv2026

Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method

Recent progress in reasoning capabilities of Multimodal Large Language Models(MLLMs) has highlighted their potential for performing complex video understanding tasks. However, in the domain of Video Anomaly Detection and Understanding (VAD&U), existing MLLM-based methods are largely limited to anomaly localization or post-hoc description, lacking explicit reasoning processes, risk awareness, and decision-oriented interpretation. To address this gap, we define a new task termed Video Anomaly Reasoning (VAR), which elevates video anomaly analysis from descriptive understanding to structured, multi-stage reasoning. VAR explicitly requires models to perform progressive reasoning over anomalous events before answering anomaly-related questions, encompassing visual perception, causal interpretation, and risk-aware decision making. To support this task, we present a new dataset with 8,641 videos, where each video is annotated with diverse question types corresponding to different reasoning depths, totaling more than 50,000 samples, making it one of the largest datasets for video anomaly. The annotations are based on a structured Perception-Cognition-Action Chain-of-Thought (PerCoAct-CoT), which formalizes domain-specific reasoning priors for video anomaly understanding. This design enables systematic evaluation of multi-stage and adaptive anomaly reasoning. In addition, we propose Anomaly-Aware Group Relative Policy Optimization to further enhance reasoning reliability under weak supervision. Building upon the proposed task and dataset, we develop an end-to-end MLLM-based VAR model termed Vad-R1-Plus, which supports adaptive hierarchical reasoning and risk-aware decision making. Extensive experiments demonstrate that the proposed benchmark and method effectively advance the reasoning capabilities of MLLMs on VAR tasks, outperforming both open-source and proprietary baselines.

cs.CV↗
arXiv2026

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.

cond-mat.mtrl-sci↗