arXiv subjects

Xiaopeng Wang

Publications and source records attributed to Xiaopeng Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Nonreciprocal parametric amplification of elastic waves in supersonic space-time modulated media

Space-time modulated elastic media, whose material properties vary in both space and time, have attracted significant attention as a promising strategy for achieving nonreciprocal propagation of elastic waves. To date, most studies have focused on systems with subsonic modulation, where the phase velocity of the modulation wave is lower than that of the guided elastic waves. In contrast, wave propagation under supersonic modulation remains largely unexplored, and the associated nonreciprocal phenomena present an open challenge. In this work, we investigate the dynamic response of elastic longitudinal waves propagating through a spatially bounded medium subjected to supersonic modulation of its elastic properties. We show that supersonic modulation gives rise to directional wavenumber bandgaps in the dispersion diagram, characterized by vanishing wavenumbers. Using a theoretical framework based on mode-coupling theory, we reveal how supersonic modulation governs amplitude modulation and frequency conversion in wave transmission and reflection at spatial interfaces. This leads to nonreciprocal parametric amplification and frequency conversion, phenomena not previously reported in bounded elastic media. Remarkably, the observed parametric amplification is not driven by an imaginary component of the wavenumber, but instead arises from the interference between counter-propagating Floquet-Bloch modes at spatial interfaces. Numerical simulations corroborate the theoretical predictions and illustrate nonreciprocal amplification and frequency conversion effects. Our findings pave the way for the design of nonreciprocal elastic wave devices with advanced functionalities such as signal processing, energy amplification, and frequency conversion.

physics.app-ph↗

arXiv2025

Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform

The rapid advancement of speech generation technology has led to the widespread proliferation of deepfake speech across social media platforms. While deepfake audio countermeasures (CMs) achieve promising results on public datasets, their performance degrades significantly in cross-domain scenarios. To advance CMs for real-world deepfake detection, we first propose the Fake Speech Wild (FSW) dataset, which includes 254 hours of real and deepfake audio from four different media platforms, focusing on social media. As CMs, we establish a benchmark using public datasets and advanced selfsupervised learning (SSL)-based CMs to evaluate current CMs in real-world scenarios. We also assess the effectiveness of data augmentation strategies in enhancing CM robustness for detecting deepfake speech on social media. Finally, by augmenting public datasets and incorporating the FSW training set, we significantly advanced real-world deepfake audio detection performance, achieving an average equal error rate (EER) of 3.54% across all evaluation sets.

cs.SD↗

arXiv2025

Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation

We propose Kling-Foley, a large-scale multimodal Video-to-Audio generation model that synthesizes high-quality audio synchronized with video content. In Kling-Foley, we introduce multimodal diffusion transformers to model the interactions between video, audio, and text modalities, and combine it with a visual semantic representation module and an audio-visual synchronization module to enhance alignment capabilities. Specifically, these modules align video conditions with latent audio elements at the frame level, thereby improving semantic alignment and audio-visual synchronization. Together with text conditions, this integrated approach enables precise generation of video-matching sound effects. In addition, we propose a universal latent audio codec that can achieve high-quality modeling in various scenarios such as sound effects, speech, singing, and music. We employ a stereo rendering method that imbues synthesized audio with a spatial presence. At the same time, in order to make up for the incomplete types and annotations of the open-source benchmark, we also open-source an industrial-level benchmark Kling-Audio-Eval. Our experiments show that Kling-Foley trained with the flow matching objective achieves new audio-visual SOTA performance among public models in terms of distribution matching, semantic alignment, temporal alignment and audio quality.

eess.AS↗

arXiv2025

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection

Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attack patterns. This limitation mainly arises because the models rely heavily on the distribution of the training data and fail to learn a decision boundary that captures the essential characteristics of forgeries. Additionally, relying solely on a classification loss makes it difficult to capture the intrinsic differences between real and fake audio. In this paper, we propose the RPRA-ADD, an integrated Reconstruction-Perception-Reinforcement-Attention networks based forgery trace enhancement-driven robust audio deepfake detection framework. First, we propose a Global-Local Forgery Perception (GLFP) module for enhancing the acoustic perception capacity of forgery traces. To significantly reinforce the feature space distribution differences between real and fake audio, the Multi-stage Dispersed Enhancement Loss (MDEL) is designed, which implements a dispersal strategy in multi-stage feature spaces. Furthermore, in order to enhance feature awareness towards forgery traces, the Fake Trace Focused Attention (FTFA) mechanism is introduced to adjust attention weights dynamically according to the reconstruction discrepancy matrix. Visualization experiments not only demonstrate that FTFA improves attention to voice segments, but also enhance the generalization capability. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on 4 benchmark datasets, including ASVspoof2019, ASVspoof2021, CodecFake, and FakeSound, achieving over 20% performance improvement. In addition, it outperforms existing methods in rigorous 3*3 cross-domain evaluations across Speech, Sound, and Singing, demonstrating strong generalization capability across diverse audio domains.

cs.SD↗

arXiv2025

Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

cs.NE↗

arXiv2025

Nonreciprocal scattering of elastic waves at time interfaces induced by spatiotemporal modulation

Spatiotemporally modulated elastic metamaterials have garnered increasing interest for their potential applications in nonreciprocal wave devices. Most existing studies, however, focus on systems where spatiotemporal modulation is continuous and infinite in time. Here, we investigate the temporal dynamics of elastic waves at time interfaces created by the sudden activation or deactivation of spatiotemporal modulation in a medium's elastic properties. By developing an ad hoc mode-coupling theory, we reveal that such time interfaces enable controlled frequency and wavenumber conversion through mode redistribution and energy pumping. Specifically, we quantitatively evaluate the temporal scattering behavior of elastic longitudinal waves under two representative spatiotemporal modulations: subsonic and supersonic. These modulations give rise to frequency and wavenumber bandgaps, respectively. We demonstrate that subsonic modulation induces nonreciprocal energy reversal, while supersonic modulation leads to nonreciprocal energy amplification. Our findings pave the way for the development of temporal elastic metamaterials with practical applications in designing one-way elastic filters, amplifiers, and frequency converters.

physics.app-ph↗

arXiv2025

High $Q^2$ Behavior of the Proton Structure Function through the Balitsky-Kovchegov Equation

Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function $ F_2(x, Q^2) $, particularly the dependence of its power constant on various kinematic variables. In this study, we analyze the proton structure function $ F_2 $ employing the analytical solution of the Balitsky-Kovchegov equation, with a focus on the high $ Q^2 $ regime and small $ x $ domains. Our results indicate that as $ Q^2 $ increases, the slope parameter $ λ$, which characterizes the growth rate of $ F_2 $, exhibits a gradual decrease, approaching a limiting value of $ λ\approx 0.41 \pm 0.01 $ for large $ Q^2 $. We suggest that this behavior of $ λ$ may be attributed to mechanisms such as gluon overlap and the suppression of phase space growth. To substantiate these conclusions, further high-precision electron-ion collision experiments are required, encompassing a broad range of $ Q^2 $ and $ x $.

hep-ph↗

arXiv2025

Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition

Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task. However, existing studies on source tracing consider only closed-set scenarios and have not considered the challenges posed by open-set conditions. In this paper, we define the Neural Codec Source Tracing (NCST) task, which is capable of performing open-set neural codec classification and interpretable ALM detection. Specifically, we constructed the ST-Codecfake dataset for the NCST task, which includes bilingual audio samples generated by 11 state-of-the-art neural codec methods and ALM-based out-ofdistribution (OOD) test samples. Furthermore, we establish a comprehensive source tracing benchmark to assess NCST models in open-set conditions. The experimental results reveal that although the NCST models perform well in in-distribution (ID) classification and OOD detection, they lack robustness in classifying unseen real audio. The ST-codecfake dataset and code are available.

cs.SD↗

arXiv2024

Deep learning selection of analogues for Mars landing sites in the Qaidam Basin, Qinghai-Tibet Plateau

Remote sensing observations and Mars rover missions have recorded the presence of beaches, salt lakes, and wind erosion landforms in Martian sediments. All these observations indicate that Mars was hydrated in its early history. There used to be oceans on Mars, but they have now dried up. Therefore, signs of previous life on Mars could be preserved in the evaporites formed during this process. The study of evaporite regions has thus become a priority area for Mars' life exploration. This study proposes a method for training similarity metrics from surface land image data of Earth and Mars, which can be used for recognition or validation applications. The method will be applied in simulating tasks to select Mars landing sites using a selecting small-scale area of the Mars analaogue the evaporite region of Qaidam Basin, Qinghai-Tibet Plateau. This learning process minimizes discriminative loss function, which makes the similarity measure smaller for images from the same location and larger for images from different locations. This study selected a Convolutional Neural Networks (CNN) based model, which has been trained to explain various changes in image appearance and identify different landforms in Mars. By identifying different landforms, priority landing sites on Mars can be selected.

physics.geo-ph↗

arXiv2024

The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits widespread, high deception, and type versatility, posing a significant challenge to current audio deepfake detection (ADD) models trained solely on vocoded data. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially constructed the Codecfake dataset, an open-source, large-scale collection comprising over 1 million audio samples in both English and Chinese, focus on ALM-based audio detection. As countermeasure, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original sharpness aware minimization (SAM), we propose the CSAM strategy to learn a domain balanced and generalized minima. In our experiments, we first demonstrate that ADD model training with the Codecfake dataset can effectively detects ALM-based audio. Furthermore, our proposed generalization countermeasure yields the lowest average equal error rate (EER) of 0.616% across all test conditions compared to baseline models. The dataset and associated code are available online.

cs.SD↗

arXiv2024

Quantifying Global Food Trade: A Net Caloric Content Approach to Food Trade Network Analysis

As the global population and the per capita demand for resource intensive diets continues to grow, the corresponding increase in food demand challenges the global food system, enhancing its reliance on trade. Most previous research typically constructed either unweighted networks or weighted solely by tonnage to represent food trade, and focused on bilateral trade relationships between pairs of countries. This study investigates the properties of global food trade constructed in terms of total food calories associated with all the main food products exchanged along each trade link (edge of the food trade network). Utilizing data from the Food and Agriculture Organization between 1986 and 2022, we construct a directed, weighted network of net caloric flows between countries. This approach highlights the importance of considering nutritional value in discussions of food security and trade policies, offering a more holistic view of global food trade dynamics. Our analysis reveals significant heterogeneity in trade patterns, with certain countries emerging as major exporters or importers of food calories. Moreover, we employ network measures, including network connectivity, network heterogeneity, network modularity, and node correlation similarity, to elucidate the structural dynamics of global net food calorie trade networks that are relevant to the stability and resilience of the global food system. Our work provides a more nuanced understanding of global food trade dynamics, emphasizing the need for comprehensive strategies to enhance the resilience and sustainability of food trade networks.

econ.GN↗

arXiv2024

The FruitShell French synthesis system at the Blizzard 2023 Challenge

This paper presents a French text-to-speech synthesis system for the Blizzard Challenge 2023. The challenge consists of two tasks: generating high-quality speech from female speakers and generating speech that closely resembles specific individuals. Regarding the competition data, we conducted a screening process to remove missing or erroneous text data. We organized all symbols except for phonemes and eliminated symbols that had no pronunciation or zero duration. Additionally, we added word boundary and start/end symbols to the text, which we have found to improve speech quality based on our previous experience. For the Spoke task, we performed data augmentation according to the competition rules. We used an open-source G2P model to transcribe the French texts into phonemes. As the G2P model uses the International Phonetic Alphabet (IPA), we applied the same transcription process to the provided competition data for standardization. However, due to compiler limitations in recognizing special symbols from the IPA chart, we followed the rules to convert all phonemes into the phonetic scheme used in the competition data. Finally, we resampled all competition audio to a uniform sampling rate of 16 kHz. We employed a VITS-based acoustic model with the hifigan vocoder. For the Spoke task, we trained a multi-speaker model and incorporated speaker information into the duration predictor, vocoder, and flow layers of the model. The evaluation results of our system showed a quality MOS score of 3.6 for the Hub task and 3.4 for the Spoke task, placing our system at an average level among all participating teams.

eess.AS↗

arXiv2024

Device for detection of activity-dependent changes in neural spheroids at MHz and GHz frequencies

Intracellular processes triggered by neural activity include changes in ionic concentrations, protein release, and synaptic vesicle cycling. These processes play significant roles in neurological disorders. The beneficial effects of brain stimulation may also be mediated through intracellular changes. There is a lack of label-free techniques for monitoring activity-dependent intracellular changes. Electromagnetic (EM) waves at frequencies larger than 1x10^6 Hz (1 MHz) were previously used to probe intracellular contents of cells, as cell membrane becomes transparent at this frequency range. EM waves interact with membranes of intracellular organelles, proteins, and water in the MHz-GHz range. In this work, we developed a device for probing the interaction between intracellular contents of active neurons and EM waves. The device used an array of grounded coplanar waveguides (GCPWs) to deliver EM waves to a three-dimensional (3D) spheroid of rat cortical neurons. Neural activity was evoked using optogenetics, with synchronous detection of propagation of EM waves. Broadband measurements were conducted in the MHz-GHz range to track changes in transmission coefficients. Neuronal activity was found to reversibly alter EM wave transmission. Pharmacological suppression of neuronal activity abolished changes in transmission. Time constants of changes in transmission were in the range of seconds to tens of seconds, suggesting the presence of relatively slow, activity-dependent intracellular processes. This study provides the first evidence that EM transmission through neuronal tissue is activity-dependent in MHz-GHz range. Device developed in this work may find future applications in studies of the mechanisms of neurological disorders and the development of new therapies.

q-bio.NC↗

arXiv2024

DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech

In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have also gained attention. However, current DiT speech models treat Mel spectrograms as general images, which overlooks the specific acoustic properties of speech. To address these limitations, we propose a method called Directional Patch Interaction for Text-to-Speech (DPI-TTS), which builds on DiT and achieves fast training without compromising accuracy. Notably, DPI-TTS employs a low-to-high frequency, frame-by-frame progressive inference approach that aligns more closely with acoustic properties, enhancing the naturalness of the generated speech. Additionally, we introduce a fine-grained style temporal modeling method that further improves speaker style similarity. Experimental results demonstrate that our method increases the training speed by nearly 2 times and significantly outperforms the baseline models.

cs.SD↗

arXiv2024

Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0

Speech synthesis technology has posed a serious threat to speaker verification systems. Currently, the most effective fake audio detection methods utilize pretrained models, and integrating features from various layers of pretrained model further enhances detection performance. However, most of the previously proposed fusion methods require fine-tuning the pretrained models, resulting in excessively long training times and hindering model iteration when facing new speech synthesis technology. To address this issue, this paper proposes a feature fusion method based on the Mixture of Experts, which extracts and integrates features relevant to fake audio detection from layer features, guided by a gating network based on the last layer feature, while freezing the pretrained model. Experiments conducted on the ASVspoof2019 and ASVspoof2021 datasets demonstrate that the proposed method achieves competitive performance compared to those requiring fine-tuning.

cs.SD↗

arXiv2024

Measurement and Modeling on Terahertz Channels in Rain

The Terahertz (THz) frequency band offers a wide range of bandwidths, from tens to hundreds of gigahertz (GHz) and also supports data speeds of several terabits per second (Tbps). Because of this, maintaining THz channel reliability and efficiency in adverse weather conditions is crucial. Rain, in particular, disrupts THz channel propagation significantly and there is still lack of comprehensive investigations due to the involved experimental difficulties. This work explores how rain affects THz channel performance by conducting experiments in a rain emulation chamber and under actual rainy conditions outdoors. We focus on variables like rain intensity, raindrop size distribution (RDSD), and the channel's gradient height. We observe that the gradient height (for air-to-ground channel) can induce changes of the RDSD along the channel's path, impacting the precision of modeling efforts. To address this, we propose a theoretical model, integrating Mie scattering theory with considerations of channel's gradient height. Both our experimental and theoretical findings confirm this model's effectiveness in predicting THz channel behavior in rainy conditions. This work underscores the necessary in incorporating the variation of RDSD when THz channel travels in scenarios involving ground-to-air or air-to-ground communications.

physics.app-ph↗

arXiv2024

Exploring the Impact of anti-shadowing effect on Unintegrated Gluon Distributions in the MD-BFKL Equation

This paper presents a comprehensive analysis of the MD-BFKL equation, considering both shadowing and anti-shadowing effects in gluon recombination processes. By deriving analytical expressions for unintegrated gluon distributions through the solution of the MD-BFKL equation, with and without the incorporation of anti-shadowing effect, we offer new insights into the influence of these effects on the behavior of unintegrated gluon distributions. Our results, when compared to the CT18NLO gluon distribution function, demonstrate that the anti-shadowing effect has a notably stronger impact on the characteristics of unintegrated gluon distributions, particularly in regions of high rapidity and momentum. This work significantly contributes to the understanding of gluon recombination mechanisms and their implications in high energy physics.

hep-ph↗

arXiv2024

Excellent and CO$_2$$_{0.85}$Nd$_{0.1}$Cu$_{0.05}$O$_{2-δ}$-Nd$_x$Sr$_{1-x}$Fe$_{1-y}$Cu$_y$O$_{3-δ}$ dual-phase oxygen transport membranes

Oxygen transport membranes(OTMs)have provided great opportunities in the last decades but are suffering from the trade-off effect between stability and oxygen permeability. Here, we report a group of new planar dual-phase mixed ionic-electronic conducting (MIEC) OTMs consisting of CO$_2$$_{0.85}$Nd$_{0.1}$Cu$_{0.05}$O$_2$ (CNCO) and Nd$_x$Sr$_{1-x}$Fe$_{1-y}$Cu$_y$O$_3$(NSFCO; $x = 0.4, 0.6$; $y = 0.05, 0.1$) phases, showing excellent oxygen permeability while comparable CO$_2$-resistant stability. The substitution of Cu as a bifunctional additive decreases the sintering temperature and enhances bulk diffusion and oxygen permeability with the co-doping of Nd.The oxygen permeation fluxes reached 2.62 and 1.52 mL min$^{-1}$ cm$^{-2}$ at 1000$^\circ$C through the optimal 60wt%Ce0.85Nd0.1Cu0.05O2-40wt%Nd0.4Sr0.6Fe0.9Cu0.1O3 (CNCO-NSFCO41) composition with He and CO$_2$ sweeping, respectively, higher than all reported dense dual-phase OTMs. Such excellent CO$_2$-tolerant permeability meets the needs of potential industrial applications. Analysis with Zhu's oxygen permeation model shows lower bulk diffusion resistance of CNCO-NSFCO41 than that of reported 60wt%Ce0.85Pr0.1Cu0.05O2-40wt%Pr0.4Sr0.6Fe0.9Cu0.1O3(CPCO-PSFCO41)and more limitation by the interfacial exchange at high temperature. All the prepared OTMs also show good long-term stability over 100 hours in both atmospheres. Our results confirm the excellent oxygen permeability and stability under a high-concentration CO2 atmosphere, providing a material candidate for CO2 capture in oxyfuel combustion.

cond-mat.mtrl-sci↗