arXiv subjects

Yang Xiao

Publications and source records attributed to Yang Xiao.

At least 181 records · Page 10Linked to original sources

arXiv2025

Fine-Grained 1-Day Vulnerability Detection in Binaries via Patch Code Localization

1-day vulnerabilities in binaries have become a major threat to software security. Patch presence test is one of the effective ways to detect the vulnerability. However, existing patch presence test works do not perform well in practical scenarios due to the interference from the various compilers and optimizations, patch-similar code blocks, and irrelevant functions in stripped binaries. In this paper, we propose a novel approach named PLocator, which leverages stable values from both the patch code and its context, extracted from the control flow graph, to accurately locate the real patch code in the target function, offering a practical solution for real-world vulnerability detection scenarios. To evaluate the effectiveness of PLocator, we collected 73 CVEs and constructed two comprehensive datasets ($Dataset_{-irr}$ and $Dataset_{+irr}$), comprising 1,090 and 27,250 test cases at four compilation optimization levels and two compilers with three different experiments, i.e., Same, XO (cross-optimizations), and XC (cross-compilers). The results demonstrate that PLocator achieves an average TPR of 88.2% and FPR of 12.9% in a short amount of time, outperforming state-of-the-art approaches by 26.7% and 63.5%, respectively, indicating that PLocator is more practical for the 1-day vulnerability detection task.

cs.CR↗

arXiv2025

Relativistic chiral nuclear forces: status and prospects

Understanding nuclear structure, reactions, and the properties of neutron stars from \textit{ab initio} calculations from the nucleon degrees of freedom has always been a primary goal of nuclear physics, in which the microscopic nuclear force serves as the fundamental input. So far, the Weinberg chiral nuclear force, first proposed by the Nobel laureate Weinberg, has become the \textit{de facto} standard input for nuclear \textit{ab initio} studies. However, compared to their non-relativistic counterparts, relativistic \textit{ab initio} calculations, which describe better nuclear observables, have only begun. The lack of modern relativistic nucleon-nucleon interactions is an important issue restricting their development. In this work, we briefly review the development and status of the Weinberg chiral nuclear force, as well as its limitations. We further present a concise introduction to the relativistic chiral nuclear force, show its description of the scattering phase shifts and observables such as differential cross sections, and demonstrate its unique features. Additionally, we show that the relativistic framework could be naturally extended to the antinucleon-nucleon interaction.

nucl-th↗

arXiv2025

Dark Experience for Incremental Keyword Spotting

Spoken keyword spotting (KWS) is crucial for identifying keywords within audio inputs and is widely used in applications like Apple Siri and Google Home, particularly on edge devices. Current deep learning-based KWS systems, which are typically trained on a limited set of keywords, can suffer from performance degradation when encountering new domains, a challenge often addressed through few-shot fine-tuning. However, this adaptation frequently leads to catastrophic forgetting, where the model's performance on original data deteriorates. Progressive continual learning (CL) strategies have been proposed to overcome this, but they face limitations such as the need for task-ID information and increased storage, making them less practical for lightweight devices. To address these challenges, we introduce Dark Experience for Keyword Spotting (DE-KWS), a novel CL approach that leverages dark knowledge to distill past experiences throughout the training process. DE-KWS combines rehearsal and distillation, using both ground truth labels and logits stored in a memory buffer to maintain model performance across tasks. Evaluations on the Google Speech Command dataset show that DE-KWS outperforms existing CL baselines in average accuracy without increasing model size, offering an effective solution for resource-constrained edge devices. The scripts are available on GitHub for the future research.

eess.AS↗

arXiv2025

Leveraging LLM and Text-Queried Separation for Noise-Robust Sound Event Detection

Sound Event Detection (SED) is challenging in noisy environments where overlapping sounds obscure target events. Language-queried audio source separation (LASS) aims to isolate the target sound events from a noisy clip. However, this approach can fail when the exact target sound is unknown, particularly in noisy test sets, leading to reduced performance. To address this issue, we leverage the capabilities of large language models (LLMs) to analyze and summarize acoustic data. By using LLMs to identify and select specific noise types, we implement a noise augmentation method for noise-robust fine-tuning. The fine-tuned model is applied to predict clip-wise event predictions as text queries for the LASS model. Our studies demonstrate that the proposed method improves SED performance in noisy environments. This work represents an early application of LLMs in noise-robust SED and suggests a promising direction for handling overlapping events in SED. Codes and pretrained models are available at https://github.com/apple-yinhan/Noise-robust-SED.

eess.AS↗

arXiv2025

UCIL: An Unsupervised Class Incremental Learning Approach for Sound Event Detection

This work explores class-incremental learning (CIL) for sound event detection (SED), advancing adaptability towards real-world scenarios. CIL's success in domains like computer vision inspired our SED-tailored method, addressing the unique challenges of diverse and complex audio environments. Our approach employs an independent unsupervised learning framework with a distillation loss function to integrate new sound classes while preserving the SED model consistency across incremental tasks. We further enhance this framework with a sample selection strategy for unlabeled data and a balanced exemplar update mechanism, ensuring varied and illustrative sound representations. Evaluating various continual learning methods on the DCASE 2023 Task 4 dataset, we find that our research offers insights into each method's applicability for real-world SED systems that can have newly added sound classes. The findings also delineate future directions of CIL in dynamic audio settings.

eess.AS↗

arXiv2025

Exploring Text-Queried Sound Event Detection with Audio Source Separation

In sound event detection (SED), overlapping sound events pose a significant challenge, as certain events can be easily masked by background noise or other events, resulting in poor detection performance. To address this issue, we propose the text-queried SED (TQ-SED) framework. Specifically, we first pre-train a language-queried audio source separation (LASS) model to separate the audio tracks corresponding to different events from the input audio. Then, multiple target SED branches are employed to detect individual events. AudioSep is a state-of-the-art LASS model, but has limitations in extracting dynamic audio information because of its pure convolutional structure for separation. To address this, we integrate a dual-path recurrent neural network block into the model. We refer to this structure as AudioSep-DP, which achieves the first place in DCASE 2024 Task 9 on language-queried audio source separation (objective single model track). Experimental results show that TQ-SED can significantly improve the SED performance, with an improvement of 7.22\% on F1 score over the conventional framework. Additionally, we setup comprehensive experiments to explore the impact of model complexity. The source code and pre-trained model are released at https://github.com/apple-yinhan/TQ-SED.

eess.AS↗

arXiv2024

Reexamination of antinucleon-nucleon interactions in covariant chiral effective field theory

Motivated by the recent progress in developing high-precision relativistic chiral nucleon-nucleon interactions, we study the antinucleon-nucleon interaction in a hybrid approach where the real part of the potential is constructed in the leading-order covariant chiral effective field theory, and the imaginary part is described following the procedure adopted in the heavy baryon chiral effective field theory. The phase shifts and inelasticities with $J\leq 1$ are obtained and compared to those calculated in the next-to-leading order heavy baryon chiral effective field theory. For most partial waves, the descriptions of phase shifts and inelasticities in the hybrid approach are comparable to those in the next-to-leading order heavy baryon chiral effective field theory, confirming the relatively faster convergence of the relativistic approach observed in the nucleon-nucleon sector. In addition, we search for bound states/resonances near the $\bar{N}N$ threshold and find several structures that can be associated with those states recently observed by the BESIII Collaboration.

nucl-th↗

arXiv2024

Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction

Federated learning is known for its capability to safeguard the participants' data privacy. However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples from model updates. The state-of-the-art attacks either rely on computation-intensive iterative optimization methods to reconstruct each input batch, making scaling difficult, or involve the malicious parameter server adding extra modules before the global model architecture, rendering the attacks too conspicuous and easily detectable. To overcome these limitations, we propose Scale-MIA, a novel MIA capable of efficiently and accurately reconstructing local training samples from the aggregated model updates, even when the system is protected by a robust secure aggregation (SA) protocol. Scale-MIA utilizes the inner architecture of models and identifies the latent space as the critical layer for breaching privacy. Scale-MIA decomposes the complex reconstruction task into an innovative two-step process. The first step is to reconstruct the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted linear layers. Then in the second step, the LSRs are fed into a fine-tuned generative decoder to reconstruct the whole input batch. We implemented Scale-MIA on commonly used machine learning models and conducted comprehensive experiments across various settings. The results demonstrate that Scale-MIA achieves excellent performance on different datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency on a larger scale compared to state-of-the-art MIAs. Our code is available at https://github.com/unknown123489/Scale-MIA.

cs.LG↗

arXiv2024

WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System

This work aims to advance sound event detection (SED) research by presenting a new large language model (LLM)-powered dataset namely wild domestic environment sound event detection (WildDESED). It is crafted as an extension to the original DESED dataset to reflect diverse acoustic variability and complex noises in home settings. We leveraged LLMs to generate eight different domestic scenarios based on target sound categories of the DESED dataset. Then we enriched the scenarios with a carefully tailored mixture of noises selected from AudioSet and ensured no overlap with target sound. We consider widely popular convolutional neural recurrent network to study WildDESED dataset, which depicts its challenging nature. We then apply curriculum learning by gradually increasing noise complexity to enhance the model's generalization capabilities across various noise levels. Our results with this approach show improvements within the noisy environment, validating the effectiveness on the WildDESED dataset promoting noise-robust SED advancements.

eess.AS↗

arXiv2024

MORSE: An Efficient Homomorphic Secret Sharing Scheme Enabling Non-Linear Operation

Homomorphic secret sharing (HSS) enables two servers to locally perform functions on encrypted data directly and obtain the results in the form of shares. A Paillier-based HSS solution seamlessly achieves multiplicative homomorphism and consumes less communication costs. Unfortunately, existing Paillier-based HSS schemes suffer from a large private key size, potential calculation error, expensive computation and storage overhead, and only valid on linear operations (e.g., addition and multiplication). To this end, inspired by the Paillier cryptosystem with fast encryption and decryption, we propose MORSE, an efficient homomorphic secret sharing scheme enabling non-linear operation, which enjoys a small key size, no calculation error and low overhead. In terms of functions, MORSE supports addition, subtraction, multiplication, scalar-multiplication, and comparison. Particularly, we carefully design two conversion protocols achieving the mutual conversion between one Paillier ciphertext and two secret shares, which allows MORSE to continuously perform the above operations. Rigorous analyses demonstrate that MORSE securely outputs correct results. Experimental results show that MORSE makes a runtime improvement of up to 9.3 times in terms of secure multiplication, and a communication costs reduction of up to 16.6% in secure comparison, compared to the state-of-the-art.

cs.CR↗

arXiv2024

Long-distance strong coupling of magnon and photon: Effect of multi-mode waveguide

Coupled mode theory predicts that the long-distance coupling between two distant harmonic oscillators is in the weak coupling regime. However, a recent experimental measurement observed strong coupling of magnon and critically-driven photon with a distance of over two meters. To explain the discrepancy between theory and experiment, we study long-distance coupling of magnon and photon mediated by a multi-mode waveguide. Our results show that strong coupling is achieved only when both critical coupling and multi-mode waveguide are involved. The former reduces the damping while the latter enhances the coupling strength by increasing the pathways of coupling magnon and photon. Our theory and results pave the way for understanding the long-distance coherence and designing the magnon-based distributed quantum networks.

quant-ph↗

arXiv2024

Estimating the Uncertainty of Cosmological First Order Phase Transitions with Numerical Simulations of Bubble Nucleation

In order to study the validity of analytical formulas used in the calculation of characteristic physical quantities related to vacuum bubbles, we conduct several numerical simulations of bubble kinematics in the context of cosmological first-order phase transitions to determine potentially existing systematic uncertainties. By comparing with the analytical results, we obtain the following observations: (1) The simulated false vacuum fraction will approach the theoretical one with increasing simulated volume. When the side length of the cubic simulation volume becomes larger than 14.5 $β_{th}^{-1}$ , the simulated results do not change significantly; (2) The theoretical expected total number of bubbles do not agree with the simulated ones, which may be caused by the inconsistent use of the false vacuum fraction formula; (3) The different nucleation rate prefactors do not affect the bubble kinetics much; (4) The lifetime distribution in the sound shell model does not obey an exponential distribution, in such a way as to cause a suppression in the gravitational wave spectra.

hep-ph↗

arXiv2024

Generating three-photon Rabi oscillations without a large-detuning condition

It is well known that in the quantum Rabi model, a three-photon resonance occurs when the cavity field bare frequency is about 1/3 of the atomic transition frequency. In this manuscript, we show that the resonance can also be generated in the absence of the 1/3 condition by employing an artificial atom with tunable transition frequency. To realize the protocol, the modulation frequency should be comparable to the cavity frequency in order to induce a counter-rotating interaction in the effective Hamiltonian. In this way, three-photon Rabi oscillations can be observed in a small-detuning regime, thus avoiding the excitation of high-energy states. We derive an effective Hamiltonian (equivalent to the anisotropic Rabi model Hamiltonian) to determine the magnitude of the energy splitting and the resonance position. Numerical simulations results show that the protocol not only generates a three-photon resonance, but also has a detectable output photon flux. We hope the protocol can be exploited for the realization of Fock-state sources and the generation of multiparticle entanglement.

quant-ph↗

arXiv2024

Stellar $β$-decay rate of $s$-process branching-point $^{204}$Tl: forbidden transitions

We propose a theoretical method to calculate the stellar $β$-decay rates of nuclei in stellar environments with high temperature and density, based on the projected shell model, where contributions from both allowed and first-forbidden transitions are taken into account. As the first example, the stellar $β$-decay rate of one of the last $s$-process branching-point nuclei, $^{204}$Tl, is calculated and studied, where all related transitions are first-forbidden transitions. For the terrestrial case, the ground-state to ground-state transition is unique first-forbidden transition, which is described reasonably by our calculations. At the typical $s$-process temperature ($T\approx 0.3$ GK), non-unique first-forbidden transitions from thermally populated excited states of the parent nucleus are involved, the effective rate from our calculations is much lower than the one from the widely used data tables by Takahashi and Yokoi. Effect of the quenching factors for nuclear matrix elements in first-forbidden transitions on the stellar $β$-decay rates is discussed as well.

nucl-th↗

arXiv2024

NGP-RT: Fusing Multi-Level Hash Features with Lightweight Attention for Real-Time Novel View Synthesis

This paper presents NGP-RT, a novel approach for enhancing the rendering speed of Instant-NGP to achieve real-time novel view synthesis. As a classic NeRF-based method, Instant-NGP stores implicit features in multi-level grids or hash tables and applies a shallow MLP to convert the implicit features into explicit colors and densities. Although it achieves fast training speed, there is still a lot of room for improvement in its rendering speed due to the per-point MLP executions for implicit multi-level feature aggregation, especially for real-time applications. To address this challenge, our proposed NGP-RT explicitly stores colors and densities as hash features, and leverages a lightweight attention mechanism to disambiguate the hash collisions instead of using computationally intensive MLP. At the rendering stage, NGP-RT incorporates a pre-computed occupancy distance grid into the ray marching strategy to inform the distance to the nearest occupied voxel, thereby reducing the number of marching points and global memory access. Experimental results show that on the challenging Mip-NeRF360 dataset, NGP-RT achieves better rendering quality than previous NeRF-based methods, achieving 108 fps at 1080p resolution on a single Nvidia RTX 3090 GPU. Our approach is promising for NeRF-based real-time applications that require efficient and high-quality rendering.

cs.CV↗

arXiv2024

Prolonged Phase Segregation of Mixed-Halide Perovskite Nanocrystals in the Dark

A critical issue hindering the potential applications of semiconductor mixed-halide perovskites is the phase segregation effect, wherein localized regions enriched with one type of halide anions would be formed upon continuous photogeneration of the excited-state charge carriers. These unexpected phases are capable of remixing again in the dark under the entropic driving force, the process of which are now being exclusively studied after mixed-halide perovskites have arrived at the final stage of complete phase segregation. Here we show that after the removal of laser excitation from a solid film of mixed-halide perovskite nanocrystals with partial phase segregation, the iodide- and bromide-rich regions can continuously grow in the dark for a prolonged time period of several minutes. We propose that this dark phase segregation is sustained by the local electric fields associated with the surface-trapped charge carriers, whose slow dissipation out of mixed-halide perovskite nanocrystals causes a delayed occurrence of the reversal phase remixing process.

cond-mat.mtrl-sci↗

arXiv2024

A Precise Fitting Formula for Gravitational Wave Spectra from Phase Transitions

Obtaining a precise form for the predicted gravitational wave (GW) spectrum from a phase transition is a topic of great relevance for beyond Standard Model (BSM) physicists. Currently, the most sophisticated semi-analytic framework for estimating the dominant contribution to the spectrum is the sound shell model; however, full calculations within this framework can be computationally expensive, especially for large-scale scans. The community therefore generally manages with fit functions to the GW spectrum, the most widely used of which is a single broken power law. We provide a more precise fit function based on the sound shell model: our fit function features a double broken power law with two frequency breaks corresponding to the two characteristic length scales of the problem -- inter-bubble spacing and thickness of sound shells, the second of which is neglected in the single broken power law fit. Compared to previously proposed fits, we demonstrate that our fit function more faithfully captures the GW spectrum coming from a full calculation of the sound shell model, over most of the space of the thermodynamic parameters governing the phase transition. The physical origins of the fit parameters and their dependence on the thermodynamic parameters are studied in the underlying sound shell model: in particular, we perform a series of detailed scans for these quantities over the plane of the strength of the phase transition ($α$) and the bubble wall velocity ($v_w$). Wherever possible, we comment on the physical interpretations of these scans. The result of our study can be used to generate accurate GW spectra with our fit function, given initial inputs of $α$, $v_w$, $β/H$ (nucleation rate parameter) and $T_n$ (nucleation temperature) for the relevant BSM scenario.

hep-ph↗

arXiv2024

FMSG-JLESS Submission for DCASE 2024 Task4 on Sound Event Detection with Heterogeneous Training Dataset and Potentially Missing Labels

This report presents the systems developed and submitted by Fortemedia Singapore (FMSG) and Joint Laboratory of Environmental Sound Sensing (JLESS) for DCASE 2024 Task 4. The task focuses on recognizing event classes and their time boundaries, given that multiple events can be present and may overlap in an audio recording. The novelty this year is a dataset with two sources, making it challenging to achieve good performance without knowing the source of the audio clips during evaluation. To address this, we propose a sound event detection method using domain generalization. Our approach integrates features from bidirectional encoder representations from audio transformers and a convolutional recurrent neural network. We focus on three main strategies to improve our method. First, we apply mixstyle to the frequency dimension to adapt the mel-spectrograms from different domains. Second, we consider training loss of our model specific to each datasets for their corresponding classes. This independent learning framework helps the model extract domain-specific features effectively. Lastly, we use the sound event bounding boxes method for post-processing. Our proposed method shows superior macro-average pAUC and polyphonic SED score performance on the DCASE 2024 Challenge Task 4 validation dataset and public evaluation dataset.

eess.AS↗