arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2025

Every Image Listens, Every Image Dances: Music-Driven Image Animation

Image animation has become a promising area in multimodal research, with a focus on generating videos from reference images. While prior work has largely emphasized generic video generation guided by text, music-driven dance video generation remains underexplored. In this paper, we introduce MuseDance, an innovative end-to-end model that animates reference images using both music and text inputs. This dual input enables MuseDance to generate personalized videos that follow text descriptions and synchronize character movements with the music. Unlike existing approaches, MuseDance eliminates the need for complex motion guidance inputs, such as pose or depth sequences, making flexible and creative video generation accessible to users of all expertise levels. To advance research in this field, we present a new multimodal dataset comprising 2,904 dance videos with corresponding background music and text descriptions. Our approach leverages diffusion-based methods to achieve robust generalization, precise control, and temporal consistency, setting a new baseline for the music-driven image animation task.

cs.CV↗

arXiv2025

Filtering Discomforting Recommendations with Large Language Models

Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. Based on the edited profile, DiscomfortFilter facilitates the discomforting recommendations filtering in a plug-and-play manner, maintaining flexibility and transparency. The constructed preference profile improves LLM reasoning and simplifies user alignment, enabling a 3.8B open-source LLM to rival top commercial models in an offline proxy task. A one-week user study with 24 participants demonstrated the effectiveness of DiscomfortFilter, while also highlighting its potential impact on platform recommendation outcomes. We conclude by discussing the ongoing challenges, highlighting its relevance to broader research, assessing stakeholder impact, and outlining future research directions.

cs.IR↗

arXiv2025

Double Majorana Vortex Flat Bands in the Topological Dirac Superconductor

Vortex lines, known as topological defects, are cable of trapping Majorana modes in superconducting topological materials. Previous studies have primarily focused on topological bands with conventional s-wave pairing. However, topological Dirac semimetals exhibiting a unique orbital texture can favor unconventional pairing when electronic correlations are significant. The topology of vortices in these systems has yet to be explored. In this work, we investigate the vortex bound states in superconducting Dirac semimetals, with a particular focus on the orbital-singlet unconventional pairing, which generates higher-order Majorana hinge modes. Remarkably, we identify robust double Majorana vortex flat bands at zero energy. In type-I Dirac semimetals, these Majorana flat bands are located between the projections of two superconduting Dirac points. In contrast, in type-II Dirac semimetals, they extend across the entire 1D Brillouin zone. These double flat bands arise from a nontrivial $\mathbb{Z}_2$ topology defined by an effective particle-hole symmetry and are protected by the four-fold rotational symmetry. Additionally, we observe that moving the vortex line close to a hinge can trivialize the higher-order Majorana arc on the hinge, leaving a single Majorana mode at the vortex core due to the hybridization of Majorana modes. Finally, we discuss the potential experimental implications for correlated Dirac semimetals, such as electron-doped iron-based superconductors.

cond-mat.supr-con↗

arXiv2025

The Scaling Law for LoRA Base on Mutual Information Upper Bound

LoRA (Low-Rank Adaptation) is a widely used model fine-tuning method. In fine-tuning, the law among model performance, model parameters, and data complexity has been a focal issue in the field. Existing methods often leverage external metrics (such as cross-entropy or perplexity) to evaluate model performance. In the fine-tuning process for large models, two types of knowledge are typically involved: the frozen, general knowledge acquired by the model during pre-training and the new knowledge learned through the LoRA module from the current data. Generally, the less LoRA's learned knowledge relies on the large model, the more it captures the specific knowledge of new data, thereby enhancing its adaptability to new tasks. However, external metrics do not readily capture the dependency relationship between these two types of knowledge. Therefore, we designed an internal metric based on the Mutual Information Upper Bound (MIUB) theory to investigate the scaling law of large-model LoRA fine-tuning. In our experiments, we validated this approach on benchmark datasets, using the Llama3-8B and Phi3-3B models. The results show that the proposed MIUB metric aligns more accurately and stably with the scaling law of LoRA fine-tuning compared to cross-entropy and perplexity.

cs.LG↗

arXiv2024

Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning

This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability inherent to platoon systems. The framework first develops a physics-based controller to model vehicle dynamics, using driving speed as input to optimize safety and efficiency. Then the residual controller, based on neural network (NN) learning, enriches the prior knowledge of the physical model and corrects residuals caused by vehicle dynamics. By integrating the physical model with data-driven online learning, the PERL framework retains the interpretability and transparency of physics-based models and enhances the adaptability and precision of data-driven learning, achieving significant improvements in computational efficiency and control accuracy in dynamic scenarios. Simulation and robot car platform tests demonstrate that PERL significantly outperforms pure physical and learning models, reducing average cumulative absolute position and speed errors by up to 58.5% and 40.1% (physical model) and 58.4% and 47.7% (NN model). The reduced-scale robot car platform tests further validate the adaptive PERL framework's superior accuracy and rapid convergence under dynamic disturbances, reducing position and speed cumulative errors by 72.73% and 99.05% (physical model) and 64.71% and 72.58% (NN model). PERL enhances platoon control performance through online parameter updates when external disturbances are detected. Results demonstrate the advanced framework's exceptional accuracy and rapid convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.

cs.RO↗

arXiv2024

Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks

A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in invasive BCIs, we propose a novel spiking neural network (SNN) framework incorporating local synaptic stabilization (LSS) and channel-wise attention (CA), termed LSS-CA-SNN. LSS optimizes neuronal membrane potential dynamics, boosting classification performance, while CA refines neuronal activation, effectively reducing energy consumption. Furthermore, we introduce SpikeDrop, a data augmentation strategy designed to expand the training dataset thus enhancing model generalizability. Experiments on invasive spiking datasets recorded from two rhesus macaques demonstrated that LSS-CA-SNN surpassed state-of-the-art artificial neural networks (ANNs) in both decoding accuracy and energy efficiency, achieving 0.80-3.87% performance gains and 14.78-43.86 times energy saving. This study highlights the potential of LSS-CA-SNN and SpikeDrop in advancing invasive BCI applications.

cs.HC↗

arXiv2024

An X-ray high-frequency quasi-periodic oscillation in NGC 1365

This study presents the detection of a high-frequency quasi-periodic oscillation (QPO) in the Seyfert galaxy NGC 1365 based on observational data obtained by \emph{XMM-Newton} in January 2004. Utilizing the weighted wavelet Z-transform (WWZ) and Lomb-Scargle periodogram (LSP) methods, a QPO signal is identified at a frequency of $2.19 \times 10^{-4}\ {\rm Hz}$ (4566 s), with a confidence level of 3.6\ $σ$. The signal is notably absent in the lower 0.2 -- 1.0 keV energy band, with the primary contribution emerging from the 2.0 -- 10.0 keV band, where the confidence level reaches 3.9 $σ$. Spectral analysis shows that there are multiple absorption and emission lines in the high-energy band (> 6\ keV). The correlation between the QPO frequency ($f_{\rm QPO}$) and the mass of the central black hole ($M_{\rm BH}$) of NGC 1365 aligns with the established logarithmic trend observed across black holes, indicating the QPO is of high frequency. This discovery provides new clues for studying the generation mechanism of QPOs in Seyfert galaxies, which helps us understand the accretion process around supermassive black holes and the characteristics of strong gravitational fields in active galactic nuclei.

astro-ph.GA↗

arXiv2024

Energy-Efficient RIS-Aided Cell-Free Massive MIMO Systems: Application, Opportunities, and Challenges

Reconfigurable intelligent surfaces (RIS)-assisted cell-free massive multiple-input multiple-output (CF mMIMO) systems have emerged as a promising technology for sixth-generation communication systems. These systems capitalize on RIS to minimize power consumption, thereby achieving consistent performance and enhancing communication quality through the establishment and shaping of auxiliary signal propagation pathways between access points (APs) and users. However, integrating RIS into existing CF mMIMO infrastructures presents several technical challenges. This study delves into the signal transmission scheme and deployment architecture of RIS-aided CF mMIMO systems, addressing inherent challenges such as interference induced by RIS and the increased complexity in beam alignment. Furthermore, we address the complexities arising from the joint optimization of the reflection phase of RIS and beamforming technology at the APs, intending to fully exploit the reflection capabilities of RISs and beamforming technology to maximize the energy efficiency (EE) of the system. To overcome these challenges, we propose cooperation communication to suppress RIS-induced interference, beam tracking, and joint optimization to improve system EE. We also present specific examples of cooperative communication under the constraint of electromagnetic interference and the beam tracking of a mobile system. Additionally, we emphasize important research directions for RIS-aided CF mMIMO systems, aiming to inspire future investigations.

eess.SP↗

arXiv2024

Searching for Neutrinoless Double-Beta Decay of $^{136}$Xe with PandaX-4T

We report the search for neutrinoless double-beta decay of $^{136}$Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is performed with data from the commissioning run and the first science run. No significant excess of signal over the background is observed. A lower limit on the half-life of $^{136}$Xe neutrinoless double-beta decay is established to be $2.1 \times 10^{24}$~yr at the 90\% confidence level, with a $^{136}$Xe exposure of 44.6~kg$\cdot$year. Our result represents the most stringent constraint from a natural xenon detector to date.

nucl-ex↗

arXiv2024

SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression

Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resource-constrained scenarios. An effective approach is to eliminate redundant model parameters and computational costs while incorporating efficient expert-derived knowledge structures to achieve a balance between compression and performance. Therefore, we propose the \textit{Sememe Entanglement Encoding (SEE)} algorithm. Guided by expert prior knowledge, the model is compressed through the low-rank approximation idea. In Entanglement Embedding, basic semantic units such as sememes are represented as low-dimensional vectors, and then reconstructed into high-dimensional word embeddings through the combination of generalized quantum entanglement. We adapt the Sememe Entanglement Encoding algorithm to transformer-based models of different magnitudes. Experimental results indicate that our approach achieves stable performance while compressing model parameters and computational costs.

cs.LG↗

arXiv2024

PandaX-xT: a Multi-ten-tonne Liquid Xenon Observatory at the China Jinping Underground Laboratory

We propose a major upgrade to the existing PandaX-4T experiment in the China Jinping Underground Laboratory. The new experiment, PandaX-xT, will be a multi-ten-tonne liquid xenon, ultra-low background, and general-purpose observatory. The full-scaled PandaX-xT contains a 43-tonne liquid xenon active target. Such an experiment will significantly advance our fundamental understanding of particle physics and astrophysics. The sensitivity of dark matter direct detection will be improved by nearly two orders of magnitude compared to the current best limits, approaching the so-called "neutrino floor" for a dark matter mass above 10 GeV/$c^2$, providing a decisive test to the Weakly Interacting Massive Particle paradigm. By searching for the neutrinoless double beta decay of $^{136}$Xe isotope in the detector, the effective Majorana neutrino mass can be measured to a [10 -- 41] meV/$c^2$ sensitivity, providing a key test to the Dirac/Majorana nature of neutrino s. Astrophysical neutrinos and other ultra-rare interactions can also be measured and searched for with an unprecedented background level, opening up new windows of discovery. Depending on the findings, PandaX-xT will seek the next stage upgrade utilizing isotopic separation on natural xenon.

hep-ex↗

arXiv2024

Online Voltage Regulation of Distribution Systems with Disturbance-Action Controllers

Inverter-based distributed energy resources facilitate the advanced voltage control algorithms in the online setting with the flexibility in both active and reactive power injections. A key challenge is to continuously track the time-varying global optima with the robustness against dynamics inaccuracy and communication delay. In this paper, we introduce the disturbance-action controller by novelly formulating the voltage drop from loads as the system disturbance. The controller alternatively generates the control input and updates the parameters based on the interactions with grids. Under the linearized power flow model, we provide stability conditions of the control policy and the performance degradation to model inaccuracy. The simulation results on the radial distribution networks show the effectiveness of proposed controller under fluctuating loads and significant improvement on the robustness to these challenges. Furthermore, the ability of incorporating history information and generalization to various loads are demonstrated through extensive experiments on the parameter sensitivity.

eess.SY↗

arXiv2024

Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the ``oracle'' user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn ``forward-looking'' models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin.

cs.IR↗

arXiv2024

Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core take. To facilitate fundamental studies, the present work concerns dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators are generated by fully validated reacting flow simulations. Due to limitations of expertise-based models, a data-driven approach is adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) is used to project the simulation data onto a 2-dimensional latent space. Based on the phase trajectories in latent space, both supervised and unsupervised classifiers are proposed for datasets with well known labeling and without, respectively. For labeled datasets, we establish the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we develop a novel unsupervised classifier (GMM-DTWC) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition of complex combustion problems.

cs.LG↗

arXiv2024

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?

Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval, consequently becoming an essential component in various natural language processing systems. These systems typically focus on optimizing the embedding space by attending to the relevance of text pairs, while overlooking the Boolean logic inherent in language, which may not be captured by current training objectives. In this work, we first investigate whether current retrieval systems can comprehend the Boolean logic implied in language. To answer this question, we formulate the task of Boolean Dense Retrieval and collect a benchmark dataset, BoolQuestions, which covers complex queries containing basic Boolean logic and corresponding annotated passages. Through extensive experimental results on the proposed task and benchmark dataset, we draw the conclusion that current dense retrieval systems do not fully understand Boolean logic in language, and there is a long way to go to improve our dense retrieval systems. Furthermore, to promote further research on enhancing the understanding of Boolean logic for language models, we explore Boolean operation on decomposed query and propose a contrastive continual training method that serves as a strong baseline for the research community.

cs.IR↗

arXiv2024

Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder

As core thermal power generation equipment, steam turbines incur significant expenses and adverse effects on operation when facing interruptions like downtime, maintenance, and damage. Accurate anomaly detection is the prerequisite for ensuring the safe and stable operation of steam turbines. However, challenges in steam turbine anomaly detection, including inherent anomalies, lack of temporal information analysis, and high-dimensional data complexity, limit the effectiveness of existing methods. To address these challenges, we proposed an Enhanced Long Short-Term Memory Variational Autoencoder using Deep Advanced Features and Gaussian Mixture Model (ELSTMVAE-DAF-GMM) for precise unsupervised anomaly detection in unlabeled datasets. Specifically, LSTMVAE, integrating LSTM with VAE, was used to project high-dimensional time-series data to a low-dimensional phase space. The Deep Autoencoder-Local Outlier Factor (DAE-LOF) sample selection mechanism was used to eliminate inherent anomalies during training, further improving the model's precision and reliability. The novel deep advanced features (DAF) hybridize latent embeddings and reconstruction discrepancies from the LSTMVAE model and provide a more comprehensive data representation within a continuous and structured phase space, significantly enhancing anomaly detection by synergizing temporal dynamics with data pattern variations. These DAF were incorporated into GMM to ensure robust and effective unsupervised anomaly detection. We utilized real operating data from industry steam turbines and conducted both comparison and ablation experiments, demonstrating superior anomaly detection outcomes characterized by high accuracy and minimal false alarm rates compared with existing methods.

cs.LG↗

arXiv2024

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with $ab$ $initio$ accuracy

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with $ab$ $initio$ accuracy to address this issue. By training on structural data and corresponding $ab$ $initio$ eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with $10^6$ atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

cond-mat.mtrl-sci↗

arXiv2024

Weak antilocalization in the transition metal telluride Ta$_2$Pd$_3$Te$_5$

We report transport studies on the layered van der Waals topological crystalline insulator Ta$_2$Pd$_3$Te$_5$. The temperature-dependent resistance at high temperature is dominated by a bulk insulating gap and tend to saturate at low temperatures. Low temperature magnetotransport shows that Ta$_2$Pd$_3$Te$_5$ exhibits weak antilocatization (WAL) effect in both perpendicular orientation and parallel orientation, suggesting an contribution of the WAL effect from both topological edge states and bulk states. By measuring the anisotropic magnetoconductance and then subtracting the contribution of bulk states, the WAL effect associated with topological edge states can be revealed and analyzed quantitatively based on the two-dimensional Hikami-Larkin-Nagaoka model. Our results have important implications in understanding the WAL phenomena in Ta$_2$Pd$_3$Te$_5$.

cond-mat.mtrl-sci↗