arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 523 records · Page 29Linked to original sources

arXiv2025

Probing modified gravitational-wave propagation with extreme mass-ratio inspirals

Extreme mass-ratio inspirals (EMRIs), namely binary systems composed of a massive black hole and a compact stellar-mass object, are anticipated to be among the gravitational wave (GW) sources detected by the Laser Interferometer Space Antenna (LISA). Similarly to compact binary mergers detected by current GW detectors, EMRIs can be used as cosmic rulers to probe the expansion of the Universe. Motivated by tensions in current cosmological observations as well as by alternative models of dark energy, modified gravity theories can affect the propagation of GWs across cosmological distances, with modifications commonly parametrised in terms of two phenomenological parameters, $Ξ_0$ and $n$. In this work we adopt a Bayesian approach to constrain for the first time parametrised deviations from General Relativity using the loudest simulated EMRIs detected by LISA as dark sirens with a simulated galaxy catalog. Assuming all the cosmological parameters except $Ξ_0$ are already tightly constrained, our forecasts show that $Ξ_0$ can be constrained to a few percent level (90% C.I.) with 4 years of LISA observations, unless EMRI detection rates turn out to be closer to current pessimistic expectations. These results quickly degrade if additional cosmological parameters are inferred simultaneously, but become more robust with an extended LISA observation period of 10 years. Overall, we find that EMRIs with LISA are better at constraining modified GW propagation than current second-generation ground-based GW detectors, but they will only be comparable to third-generation detectors in the most optimistic scenarios.

astro-ph.CO↗

arXiv2025

Exploring Patterns Behind Sports

This paper presents a comprehensive framework for time series prediction using a hybrid model that combines ARIMA and LSTM. The model incorporates feature engineering techniques, including embedding and PCA, to transform raw data into a lower-dimensional representation while retaining key information. The embedding technique is used to convert categorical data into continuous vectors, facilitating the capture of complex relationships. PCA is applied to reduce dimensionality and extract principal components, enhancing model performance and computational efficiency. To handle both linear and nonlinear patterns in the data, the ARIMA model captures linear trends, while the LSTM model models complex nonlinear dependencies. The hybrid model is trained on historical data and achieves high accuracy, as demonstrated by low RMSE and MAE scores. Additionally, the paper employs the run test to assess the randomness of sequences, providing insights into the underlying patterns. Ablation studies are conducted to validate the roles of different components in the model, demonstrating the significance of each module. The paper also utilizes the SHAP method to quantify the impact of traditional advantages on the predicted results, offering a detailed understanding of feature importance. The KNN method is used to determine the optimal prediction interval, further enhancing the model's accuracy. The results highlight the effectiveness of combining traditional statistical methods with modern deep learning techniques for robust time series forecasting in Sports.

cs.LG↗

arXiv2025

A Luminous Red Optical Flare and Hard X-ray Emission in the Tidal Disruption Event AT2024kmq

We present the optical discovery and multiwavelength follow-up observations of AT2024kmq, a likely tidal disruption event (TDE) associated with a supermassive ($M_{\rm BH}\sim 10^{8} M_\odot$) black hole in a massive galaxy at $z=0.192$. The optical light curve of AT2024kmq exhibits two distinct peaks: an early fast (timescale 1 d) and luminous ($M\approx-20$ mag) red peak, then a slower (timescale 1 month) blue peak with a higher optical luminosity ($M\approx-22$ mag) and featureless optical spectra. The second component is similar to the spectroscopic class of "featureless TDEs" in the literature, and during this second component we detect highly variable, luminous ($L_X\approx 10^{44}$ erg s$^{-1}$), and hard ($f_ν\propto ν^{-1.5}$) X-ray emission. Luminous ($10^{29} $erg s$^{-1}$ Hz$^{-1}$ at 10 GHz) but unchanging radio emission likely arises from an underlying active galactic nucleus. The luminosity, timescale, and color of the early red optical peak can be explained by synchrotron emission, or alternatively by thermal emission from material at a large radius ($R\approx\mathrm{few}\times10^{15}$ cm). Possible physical origins for this early red component include an off-axis relativistic jet, and shocks from self-intersecting debris leading to the formation of the accretion disk. Late-time radio observations will help distinguish between the two possibilities.

astro-ph.HE↗

arXiv2025

Hedgehog-like spin texture in Sb-doped MnBi$_2$Te$_4$

We employ spin- and angle-resolved photoemission spectroscopy and circular-dichroism ARPES to systematically investigate the spin texture of Sb-doped MnBi$_2$Te$_4$. Our results display a hedgehog-like spin texture in this system which is signified by reversed-orienting out-of-plane spins at the Dirac gap. Our finding reveals the presence of time-reversal symmetry breaking, implying the possibility for realization of high-temperature quantum anomalous Hall effect.

cond-mat.mtrl-sci↗

arXiv2025

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective

The robustness of Graph Neural Networks (GNNs) has become an increasingly important topic due to their expanding range of applications. Various attack methods have been proposed to explore the vulnerabilities of GNNs, ranging from Graph Modification Attacks (GMA) to the more practical and flexible Graph Injection Attacks (GIA). However, existing methods face two key challenges: (i) their reliance on surrogate models, which often leads to reduced attack effectiveness due to structural differences and prior biases, and (ii) existing GIA methods often sacrifice attack success rates in undefended settings to bypass certain defense models, thereby limiting their overall effectiveness. To overcome these limitations, we propose QUGIA, a Query-based and Unnoticeable Graph Injection Attack. QUGIA injects nodes by first selecting edges based on victim node connections and then generating node features using a Bayesian framework. This ensures that the injected nodes are similar to the original graph nodes, implicitly preserving homophily and making the attack more unnoticeable. Unlike previous methods, QUGIA does not rely on surrogate models, thereby avoiding performance degradation and achieving better generalization. Extensive experiments on six real-world datasets with diverse characteristics demonstrate that QUGIA achieves unnoticeable attacks and outperforms state-of-the-art attackers. The code will be released upon acceptance.

cs.LG↗

arXiv2025

GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI

Alzheimer's Disease (AD) is a progressive, irreversible neurodegenerative disorder that often originates from Mild Cognitive Impairment (MCI). This progression results in significant memory loss and severely affects patients' quality of life. Clinical trials have consistently shown that early and targeted interventions for individuals with MCI may slow or even prevent the advancement of AD. Research indicates that accurate medical classification requires diverse multimodal data, including detailed assessment scales and neuroimaging techniques like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). However, simultaneously collecting the aforementioned three modalities for training presents substantial challenges. To tackle these difficulties, we propose GFE-Mamba, a multimodal classifier founded on Generative Feature Extractor. The intermediate features provided by this Extractor can compensate for the shortcomings of PET and achieve profound multimodal fusion in the classifier. The Mamba block, as the backbone of the classifier, enables it to efficiently extract information from long-sequence scale information. Pixel-level Bi-cross Attention supplements pixel-level information from MRI and PET. We provide our rationale for developing this cross-temporal progression prediction dataset and the pre-trained Extractor weights. Our experimental findings reveal that the GFE-Mamba model effectively predicts the progression from MCI to AD and surpasses several leading methods in the field. Our source code is available at https://github.com/Tinysqua/GFE-Mamba.

cs.CV↗

arXiv2025

Scaling Limit and Large Deviation for 3D Globally Modified Stochastic Navier-Stokes Equations with Transport Noise

We consider the globally modified stochastic (hyperviscous) Navier-Stokes equations with transport noise on 3D torus. We first establish the existence and pathwise uniqueness of the weak solutions, and then show their convergence to the solutions of the deterministic 3D globally modified (hyperviscous) Navier-Stokes equations in an appropriate scaling limit. Furthermore, we prove a large deviation principle for the stochastic globally modified hyperviscous system.

math.PR↗

arXiv2025

MiniMax-01: Scaling Foundation Models with Lightning Attention

We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens. The context window of MiniMax-Text-01 can reach up to 1 million tokens during training and extrapolate to 4 million tokens during inference at an affordable cost. Our vision-language model, MiniMax-VL-01 is built through continued training with 512 billion vision-language tokens. Experiments on both standard and in-house benchmarks show that our models match the performance of state-of-the-art models like GPT-4o and Claude-3.5-Sonnet while offering 20-32 times longer context window. We publicly release MiniMax-01 at https://github.com/MiniMax-AI.

cs.CL↗

arXiv2025

Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning

Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attributions stemming from overlooking the multifaceted nature of mission performance evaluation. Hopefully, Large Language Model (LLM) encompasses fruitful decision-making knowledge and provides a plausible tool for reward redistribution. Even so, deploying LLM in this case is non-trivial due to the misalignment between linguistic knowledge and the symbolic form requirement, together with inherent randomness and hallucinations in inference. To tackle these issues, we introduce LaRe, a novel LLM-empowered symbolic-based decision-making framework, to improve credit assignment. Key to LaRe is the concept of the Latent Reward, which works as a multi-dimensional performance evaluation, enabling more interpretable goal attainment from various perspectives and facilitating more effective reward redistribution. We examine that semantically generated code from LLM can bridge linguistic knowledge and symbolic latent rewards, as it is executable for symbolic objects. Meanwhile, we design latent reward self-verification to increase the stability and reliability of LLM inference. Theoretically, reward-irrelevant redundancy elimination in the latent reward benefits RL performance from more accurate reward estimation. Extensive experimental results witness that LaRe (i) achieves superior temporal credit assignment to SOTA methods, (ii) excels in allocating contributions among multiple agents, and (iii) outperforms policies trained with ground truth rewards for certain tasks.

cs.LG↗

arXiv2025

Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach

Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet.

cs.CV↗

arXiv2025

CycleFlow: Leveraging Cycle Consistency in Flow Matching for Speaker Style Adaptation

Voice Conversion (VC) aims to convert the style of a source speaker, such as timbre and pitch, to the style of any target speaker while preserving the linguistic content. However, the ground truth of the converted speech does not exist in a non-parallel VC scenario, which induces the train-inference mismatch problem. Moreover, existing methods still have an inaccurate pitch and low speaker adaptation quality, there is a significant disparity in pitch between the source and target speaker style domains. As a result, the models tend to generate speech with hoarseness, posing challenges in achieving high-quality voice conversion. In this study, we propose CycleFlow, a novel VC approach that leverages cycle consistency in conditional flow matching (CFM) for speaker timbre adaptation training on non-parallel data. Furthermore, we design a Dual-CFM based on VoiceCFM and PitchCFM to generate speech and improve speaker pitch adaptation quality. Experiments show that our method can significantly improve speaker similarity, generating natural and higher-quality speech.

cs.SD↗

arXiv2024

A Verbose Note on Density Functional Theory

This note is intended for expanding the details on the derivation and properties of density functional theory, in hope to make them more systematic, better motivated, and step-by-step for readers new to the domain. The note starts with basic concepts in quantum mechanics, then takes the step towards many-body systems using the tools of second quantization and Fock space, with some highlights on properties of the Coulomb system. Given these general technical preparations, the Hartree-Fock method is naturally unrolled, with expressions for various cases and quantities. Density functional theory is then presented, with a motivating reasoning of the Kohn-Sham formulation, and a connection and comparison of the expressions with the Hartree-Fock method. Lieb's celebrated 1983 paper on functional analysis on density functionals is also summarized.

physics.chem-ph↗

arXiv2024

Repeating Partial Tidal Encounters of Sun-like Stars Leading to their Complete Disruption

Stars grazing supermassive black holes on bound orbits may produce periodic flares over many passages, known as repeating partial tidal disruption events (TDEs). Here, we present 3D hydrodynamic simulations of sun-like stars over multiple tidal encounters. The star is significantly restructured and becomes less concentrated as a result of mass loss and tidal heating. The vulnerability to mass loss depends sensitively on the stellar density structure, and the strong correlation between the fractional mass loss $ΔM/M_*$ and the ratio of the central and average density $ρ_{\mathrm{c}}/\barρ$, which was initially derived in disruption simulations of main-sequence stars, also applies for stars strongly reshaped by tides. Over multiple orbits, the star loses progressively more mass in each encounter and is doomed to a complete disruption. Throughout its lifetime, the star may produce numerous weak flares (depending on the initial impact parameter), followed by a couple of luminous flares whose brightness increases exponentially. Flux-limited surveys are heavily biased toward the brightest flares, which may appear similar to the flare produced by the same star undergoing a full disruption on its first tidal encounter. This places new challenges on constraining the intrinsic TDE rates, which need to take repeating TDEs into account. Other types of stars with different initial density structures (e.g., evolved stars with massive cores) follow distinct evolution tracks, which might explain the diversity of the long-term luminosity evolution seen in recently uncovered repeaters.

astro-ph.HE↗

arXiv2024

COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection

Single-modal object detection tasks often experience performance degradation when encountering diverse scenarios. In contrast, multimodal object detection tasks can offer more comprehensive information about object features by integrating data from various modalities. Current multimodal object detection methods generally use various fusion techniques, including conventional neural networks and transformer-based models, to implement feature fusion strategies and achieve complementary information. However, since multimodal images are captured by different sensors, there are often misalignments between them, making direct matching challenging. This misalignment hinders the ability to establish strong correlations for the same object across different modalities. In this paper, we propose a novel approach called the CrOss-Mamba interaction and Offset-guided fusion (COMO) framework for multimodal object detection tasks. The COMO framework employs the cross-mamba technique to formulate feature interaction equations, enabling multimodal serialized state computation. This results in interactive fusion outputs while reducing computational overhead and improving efficiency. Additionally, COMO leverages high-level features, which are less affected by misalignment, to facilitate interaction and transfer complementary information between modalities, addressing the positional offset challenges caused by variations in camera angles and capture times. Furthermore, COMO incorporates a global and local scanning mechanism in the cross-mamba module to capture features with local correlation, particularly in remote sensing images. To preserve low-level features, the offset-guided fusion mechanism ensures effective multiscale feature utilization, allowing the construction of a multiscale fusion data cube that enhances detection performance.

cs.CV↗

arXiv2024

Deep thermalization in Gaussian continuous-variable quantum systems

We uncover emergent universality arising in the equilibration dynamics of multimode continuous-variable systems. Specifically, we study the ensemble of pure states supported on a small subsystem of a few modes, generated by Gaussian measurements on the remaining modes of a globally pure bosonic Gaussian state. We find that beginning from highly entangled, complex global states, such as random Gaussian states and product squeezed states coupled via a deep array of linear optical elements, the induced ensemble attains a universal form, independent of the choice of measurement basis: it is composed of unsqueezed coherent states whose displacements are distributed normally and isotropically, with variance depending on only the particle-number density of the system. We further show that the emergence of such a universal form is consistent with a generalized maximum entropy principle, which endows the limiting ensemble, which we call the "Gaussian Scrooge distribution", with a special quantum information-theoretic property of having minimal accessible information. Our results represent a conceptual generalization of the recently introduced notion of "deep thermalization" in discrete-variable quantum many-body systems -- a novel form of equilibration going beyond thermalization of local observables -- to the realm of continuous-variable quantum systems. Moreover, it demonstrates how quantum information-theoretic perspectives can unveil new physical phenomena and principles in quantum dynamics and statistical mechanics.

quant-ph↗

arXiv2024

Two-person zero-sum stochastic linear quadratic control problems with Markov chains and fractional Brownian motion in infinite horizon

This paper addresses a class of two-person zero-sum stochastic differential equations, which encompass Markov chains and fractional Brownian motion, and satisfy some monotonicity conditions over an infinite time horizon. Within the framework of forward-backward stochastic differential equations (FBSDEs) that describe system evolution, we extend the classical It$\rm\hat{o}$'s formula to accommodate complex scenarios involving Brownian motion, fractional Brownian motion, and Markov chains simultaneously. By applying the Banach fixed-point theorem and approximation methods respectively, we theoretically guarantee the existence and uniqueness of solutions for FBSDEs in infinite horizon. Furthermore, we apply the method for the first time to the optimal control problem in a two-player zero-sum game, deriving the optimal control strategies for both players by solving the FBSDEs system. Finally, we conduct an analysis of the impact of the cross-term $S(\cdot)$ in the cost function on the solution, revealing its crucial role in the optimization process.

math.OC↗

arXiv2024

X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructured simulation meshes. However, these models face several limitations, including scalability issues, requirement for meshing at inference, and challenges in handling long-range interactions. In this work, we introduce X-MeshGraphNet, a scalable, multi-scale extension of MeshGraphNet designed to address these challenges. X-MeshGraphNet overcomes the scalability bottleneck by partitioning large graphs and incorporating halo regions that enable seamless message passing across partitions. This, combined with gradient aggregation, ensures that training across partitions is equivalent to processing the entire graph at once. To remove the dependency on simulation meshes, X-MeshGraphNet constructs custom graphs directly from tessellated geometry files (e.g., STLs) by generating point clouds on the surface or volume of the object and connecting k-nearest neighbors. Additionally, our model builds multi-scale graphs by iteratively combining coarse and fine-resolution point clouds, where each level refines the previous, allowing for efficient long-range interactions. Our experiments demonstrate that X-MeshGraphNet maintains the predictive accuracy of full-graph GNNs while significantly improving scalability and flexibility. This approach eliminates the need for time-consuming mesh generation at inference, offering a practical solution for real-time simulation across a wide range of applications. The code for reproducing the results presented in this paper is available through NVIDIA Modulus.

cs.LG↗

arXiv2024

Hybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (UEs). However, its cooperative nature can result in intense fronthaul signaling between CPUs in large-scale networks. To reduce the inter-CPU fronthaul signaling for systems with limited fronthaul capacity, we propose a low-complexity online UE-AP association approach for scalable CFmMIMO that combines network- and user-centric clustering methodologies, relies on local channel information only, and can handle dynamic UE arrivals. Numerical results demonstrate that compared to the state-of-the-art method on fronthaul signaling minimization, our approach can save up to 94% of the fronthaul signaling load and 83% of the CPU processing power at the cost of only up to 8.6% spectral efficiency loss, or no loss in some cases.

eess.SP↗