arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 487 records · Page 27Linked to original sources

arXiv2025

Circularly Polarized Gravitational Wave Background Search with a Network of Space-borne Triangular Detectors

Circularly polarized gravitational wave backgrounds are predicted in many well-motivated models of inflation and phase transitions involving spontaneous parity violation. In this work, we investigate the detection of such parity-violating signals with the network of two space-borne triangular detectors. We derive the general analytical formula for the overlap reduction functions of networks composed of two triangular detectors, by exploiting the system's symmetrical properties under the long-wave approximation. Based on these results, we further assess the detectability of a parity-violating background with alternative LISA-Taiji network configurations. We find that the sensitivity to the parity-violating component can be significantly enhanced at low frequencies by adjusting the orientation of Taiji's constellation plane. This sensitivity gain is approximately an order of magnitude around the millihertz band, making the peak sensitivity comparable to that of the total intensity. This provides a promising opportunity to constrain various parity-violating theories in the millihertz band with upcoming space-borne detectors.

gr-qc↗

arXiv2025

Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding

If a product deviates from its desired properties in the injection moulding process, its root cause analysis can be aided by models that relate the input machine settings with the output quality characteristics. The machine learning models tested in the quality prediction are mostly black boxes; therefore, no direct explanation of their prognosis is given, which restricts their applicability in the quality control. The previously attempted explainability methods are either restricted to tree-based algorithms only or do not emphasize on the fact that some explainability methods can lead to wrong root cause identification of a product's deviation from its desired properties. This study first shows that the interactions among the multiple input machine settings do exist in real experimental data collected as per a central composite design. Then, the model-agnostic explainable AI methods are compared for the first time to show that different explainability methods indeed lead to different feature impact analysis in injection moulding. Moreover, it is shown that the better feature attribution translates to the correct cause identification and actionable insights for the injection moulding process. Being model agnostic, explanations on both random forest and multilayer perceptron are performed for the cause analysis, as both models have the mean absolute percentage error of less than 0.05% on the experimental dataset.

cs.LG↗

arXiv2025

NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM

Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous localization and mapping (SLAM) offers a more robust alternative to GPS-based positioning, enabling vehicles to determine their position using onboard sensors and surrounding environment's landmarks. Among various vehicle SLAM approaches, Rao-Blackwellized particle filter (RBPF) stands out as one of the most widely adopted methods due to its efficient solution with logarithmic complexity relative to the map size. RBPF approximates the posterior distribution of the vehicle pose using a set of Monte Carlo particles through two main steps: sampling and importance weighting. The key to effective sampling lies in solving a distribution that closely approximates the posterior, known as the sampling distribution, to accelerate convergence. Existing methods typically derive this distribution via linearization, which introduces significant approximation errors due to the inherent nonlinearity of the system. To address this limitation, we propose a novel vehicle SLAM method called \textit{N}atural Gr\textit{a}dient Gaussia\textit{n} Appr\textit{o}ximation (NANO)-SLAM, which avoids linearization errors by modeling the sampling distribution as the solution to an optimization problem over Gaussian parameters and solving it using natural gradient descent. This approach improves the accuracy of the sampling distribution and consequently enhances localization performance. Experimental results on the long-distance Sydney Victoria Park vehicle SLAM dataset show that NANO-SLAM achieves over 50\% improvement in localization accuracy compared to the most widely used vehicle SLAM algorithms, with minimal additional computational cost.

cs.RO↗

arXiv2025

Alternative LISA-TAIJI networks: Detectability of the Parity Violation in Stochastic Gravitational Wave Background

The detection of parity violation in the isotropic stochastic gravitational wave background (SGWB) will serve a crucial probe for new physics, particularly in parity-violating theories of gravity. The joint observations by the planned space-borne gravitational wave detectors, LISA and TAIJI, will offer a unique opportunity to observe such effects in the millihertz (mHz) band. This study evaluates the detectability of parity violation in the SGWB using two network configurations: LISA-TAIJIp and LISA-TAIJIm. The former configuration consists of LISA (inclined at $+60^\circ$ relative to the ecliptic plane) and TAIJIp (also inclined at $+60^\circ$), while the latter network pairs LISA with TAIJIm (inclined at $-60^\circ$). Our analysis demonstrates that the sensitivity of the LISA-TAIJIm network to parity violation in the SGWB is approximately one order of magnitude greater than that of the LISA-TAIJIp network at lower frequencies. To quantify the performance of the two networks, we evaluate the signal-to-noise ratios for different spectral shapes, including power-law, single-peak, and broken power-law models, and estimate parameter determination using the Fisher information matrix. The results confirm that LISA-TAIJIm outperforms LISA-TAIJIp in detecting the SGWB with circular polarization components, offering a superior opportunity to test parity-violating gravitational constraints on various mechanisms in the mHz band.

gr-qc↗

arXiv2025

State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments. A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism. The method builds a state-action-reward interaction model and introduces edge nodes as intermediaries for state aggregation and policy scheduling. This enables dynamic resource coordination and task allocation among multiple devices. During the modeling process, device status, task load, and network resources are jointly incorporated into the state space. The DQN is used to approximate and learn the optimal scheduling strategy. To enhance the model's ability to perceive inter-device relationships, a collaborative graph structure is introduced to model the multi-device environment and assist in decision optimization. Experiments are conducted using real-world IoT data collected from the FastBee platform. Several comparative and validation tests are performed, including energy efficiency comparisons across different scheduling strategies, robustness analysis under varying task loads, and evaluation of state dimension impacts on policy convergence speed. The results show that the proposed method outperforms existing baseline approaches in terms of average energy consumption, processing latency, and resource utilization. This confirms its effectiveness and practicality in intelligent IoT scenarios.

cs.NI↗

arXiv2025

DecETT: Accurate App Fingerprinting Under Encrypted Tunnels via Dual Decouple-based Semantic Enhancement

Due to the growing demand for privacy protection, encrypted tunnels have become increasingly popular among mobile app users, which brings new challenges to app fingerprinting (AF)-based network management. Existing methods primarily transfer traditional AF methods to encrypted tunnels directly, ignoring the core obfuscation and re-encapsulation mechanism of encrypted tunnels, thus resulting in unsatisfactory performance. In this paper, we propose DecETT, a dual decouple-based semantic enhancement method for accurate AF under encrypted tunnels. Specifically, DecETT improves AF under encrypted tunnels from two perspectives: app-specific feature enhancement and irrelevant tunnel feature decoupling.Considering the obfuscated app-specific information in encrypted tunnel traffic, DecETT introduces TLS traffic with stronger app-specific information as a semantic anchor to guide and enhance the fingerprint generation for tunnel traffic. Furthermore, to address the app-irrelevant tunnel feature introduced by the re-encapsulation mechanism, DecETT is designed with a dual decouple-based fingerprint enhancement module, which decouples the tunnel feature and app semantic feature from tunnel traffic separately, thereby minimizing the impact of tunnel features on accurate app fingerprint extraction. Evaluation under five prevalent encrypted tunnels indicates that DecETT outperforms state-of-the-art methods in accurate AF under encrypted tunnels, and further demonstrates its superiority under tunnels with more complicated obfuscation. \textit{Project page: \href{https://github.com/DecETT/DecETT}{https://github.com/DecETT/DecETT}}

cs.CR↗

arXiv2025

The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova

We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < τ~\mathrm{[d]} < 1.8$) emission lines of H$α$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients.

astro-ph.HE↗

arXiv2025

PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/.

cs.CV↗

arXiv2025

Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads

We introduce Orthus, an autoregressive (AR) transformer that excels in generating images given textual prompts, answering questions based on visual inputs, and even crafting lengthy image-text interleaved contents. Unlike prior arts on unified multimodal modeling, Orthus simultaneously copes with discrete text tokens and continuous image features under the AR modeling principle. The continuous treatment of visual signals minimizes the information loss for both image understanding and generation while the fully AR formulation renders the characterization of the correlation between modalities straightforward. The key mechanism enabling Orthus to leverage these advantages lies in its modality-specific heads -- one regular language modeling (LM) head predicts discrete text tokens and one diffusion head generates continuous image features conditioning on the output of the backbone. We devise an efficient strategy for building Orthus -- by substituting the Vector Quantization (VQ) operation in the existing unified AR model with a soft alternative, introducing a diffusion head, and tuning the added modules to reconstruct images, we can create an Orthus-base model effortlessly (e.g., within mere 72 A100 GPU hours). Orthus-base can further embrace post-training to better model interleaved images and texts. Empirically, Orthus surpasses competing baselines including Show-o and Chameleon across standard benchmarks, achieving a GenEval score of 0.58 and an MME-P score of 1265.8 using 7B parameters. Orthus also shows exceptional mixed-modality generation capabilities, reflecting the potential for handling intricate practical generation tasks.

cs.CV↗

arXiv2025

Tailoring Neel orders in Layered Topological Antiferromagnets

In the two-dimensional limit, the interplay between Neel order and band topology in van der Waals topological antiferromagnets can give rise to novel quantum phenomena in the quantum anomalous Hall state, including the cascaded quantum phase transition and spin-modulation effect. However, due to the absence of net magnetization in antiferromagnets, probing the energetically degenerate Neel orders has long remained a significant challenge. Inspired by recent advances in realizing the quantum anomalous Hall effect in AlOx-capped layered topological antiferromagnet MnBi2Te4, we demonstrate deterministic control over the Neel order through surface anisotropy engineering enabled by the AlOx capping layer. By tuning the surface anisotropy, we uncover paritydependent symmetry breaking states that manifest as distinct odd-even boundary architectures, including 180 degree domain walls or continuous spin structures. Comparative studies between AlOx-capped and pristine odd-layer MnBi2Te4 flakes using domain-resolved magnetic force microscopy reveal pronounced differences in coercivity and magnetization-reversal dynamics. Notably, an unconventional giant exchange bias, which arises from perpendicular magnetic anisotropy rather than traditional interface pinning mechanisms, is observed for the first time. Our findings establish a pathway for manipulating Neel order through surface modification in A-type antiferromagnets, offering new opportunities for spintronic devices and quantum information technologies.

cond-mat.mes-hall↗

arXiv2025

LLM-based AI Agent for Sizing of Analog and Mixed Signal Circuit

The design of Analog and Mixed-Signal (AMS) integrated circuits (ICs) often involves significant manual effort, especially during the transistor sizing process. While Machine Learning techniques in Electronic Design Automation (EDA) have shown promise in reducing complexity and minimizing human intervention, they still face challenges such as numerous iterations and a lack of knowledge about AMS circuit design. Recently, Large Language Models (LLMs) have demonstrated significant potential across various fields, showing a certain level of knowledge in circuit design and indicating their potential to automate the transistor sizing process. In this work, we propose an LLM-based AI agent for AMS circuit design to assist in the sizing process. By integrating LLMs with external circuit simulation tools and data analysis functions and employing prompt engineering strategies, the agent successfully optimized multiple circuits to achieve target performance metrics. We evaluated the performance of different LLMs to assess their applicability and optimization effectiveness across seven basic circuits, and selected the best-performing model Claude 3.5 Sonnet for further exploration on an operational amplifier, with complementary input stage and class AB output stage. This circuit was evaluated against nine performance metrics, and we conducted experiments under three distinct performance requirement groups. A success rate of up to 60% was achieved for reaching the target requirements. Overall, this work demonstrates the potential of LLMs to improve AMS circuit design.

cs.LG↗

arXiv2025

VC-LLM: Automated Advertisement Video Creation from Raw Footage using Multi-modal LLMs

As short videos have risen in popularity, the role of video content in advertising has become increasingly significant. Typically, advertisers record a large amount of raw footage about the product and then create numerous different short-form advertisement videos based on this raw footage. Creating such videos mainly involves editing raw footage and writing advertisement scripts, which requires a certain level of creative ability. It is usually challenging to create many different video contents for the same product, and manual efficiency is often low. In this paper, we present VC-LLM, a framework powered by Large Language Models for the automatic creation of high-quality short-form advertisement videos. Our approach leverages high-resolution spatial input and low-resolution temporal input to represent video clips more effectively, capturing both fine-grained visual details and broader temporal dynamics. In addition, during training, we incorporate supplementary information generated by rewriting the ground truth text, ensuring that all key output information can be directly traced back to the input, thereby reducing model hallucinations. We also designed a benchmark to evaluate the quality of the created videos. Experiments show that VC-LLM based on GPT-4o can produce videos comparable to those created by humans. Furthermore, we collected numerous high-quality short advertisement videos to create a pre-training dataset and manually cleaned a portion of the data to construct a high-quality fine-tuning dataset. Experiments indicate that, on the benchmark, the VC-LLM based on fine-tuned LLM can produce videos with superior narrative logic compared to those created by the VC-LLM based on GPT-4o.

cs.CV↗

arXiv2025

DeepAudio-V1:Towards Multi-Modal Multi-Stage End-to-End Video to Speech and Audio Generation

Currently, high-quality, synchronized audio is synthesized using various multi-modal joint learning frameworks, leveraging video and optional text inputs. In the video-to-audio benchmarks, video-to-audio quality, semantic alignment, and audio-visual synchronization are effectively achieved. However, in real-world scenarios, speech and audio often coexist in videos simultaneously, and the end-to-end generation of synchronous speech and audio given video and text conditions are not well studied. Therefore, we propose an end-to-end multi-modal generation framework that simultaneously produces speech and audio based on video and text conditions. Furthermore, the advantages of video-to-audio (V2A) models for generating speech from videos remain unclear. The proposed framework, DeepAudio, consists of a video-to-audio (V2A) module, a text-to-speech (TTS) module, and a dynamic mixture of modality fusion (MoF) module. In the evaluation, the proposed end-to-end framework achieves state-of-the-art performance on the video-audio benchmark, video-speech benchmark, and text-speech benchmark. In detail, our framework achieves comparable results in the comparison with state-of-the-art models for the video-audio and text-speech benchmarks, and surpassing state-of-the-art models in the video-speech benchmark, with WER 16.57% to 3.15% (+80.99%), SPK-SIM 78.30% to 89.38% (+14.15%), EMO-SIM 66.24% to 75.56% (+14.07%), MCD 8.59 to 7.98 (+7.10%), MCD SL 11.05 to 9.40 (+14.93%) across a variety of dubbing settings.

cs.CV↗

arXiv2025

GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion

Accurate surface reconstruction from unposed images is crucial for efficient 3D object or scene creation. However, it remains challenging, particularly for the joint camera pose estimation. Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings, but could easily fail for sparse-view scenarios without sufficient visual overlap. In this paper, we propose a new technique for pose-free surface reconstruction, which follows triplane-based signed distance field (SDF) learning but regularizes the learning by explicit points sampled from ray-based diffusion of camera pose estimation. Our key contribution is a novel Geometric Consistent Ray Diffusion model (GCRayDiffusion), where we represent camera poses as neural bundle rays and regress the distribution of noisy rays via a diffusion model. More importantly, we further condition the denoising process of RGRayDiffusion using the triplane-based SDF of the entire scene, which provides effective 3D consistent regularization to achieve multi-view consistent camera pose estimation. Finally, we incorporate RGRayDiffusion into the triplane-based SDF learning by introducing on-surface geometric regularization from the sampling points of the neural bundle rays, which leads to highly accurate pose-free surface reconstruction results even for sparse-view inputs. Extensive evaluations on public datasets show that our GCRayDiffusion achieves more accurate camera pose estimation than previous approaches, with geometrically more consistent surface reconstruction results, especially given sparse-view inputs.

cs.CV↗

arXiv2025

Radiative stabilization of the indenyl cation: Recurrent fluorescence in a closed-shell polycyclic aromatic hydrocarbon

Several small polycyclic aromatic hydrocarbons (PAHs) with closed-shell electronic structure have been identified in the cold, dark environment Taurus Molecular Cloud-1. We measure efficient radiative cooling through the combination of recurrent fluorescence (RF) and IR emission in the closed-shell indenyl cation (C$_{9}$H$_{7}^{+}$), finding good agreement with a master equation model including molecular dynamics trajectories to describe internal-energy dependent properties for RF. We find that C$_{9}$H$_{7}^{+}$ formed with up to $E_{c}=5.85$\,eV vibrational energy, which is $\approx$2\,eV above the dissociation threshold, radiatively cool rather than dissociate. The efficient radiative stabilization dynamics are likely common to other closed-shell PAHs present in space, contributing to their abundance.

physics.chem-ph↗

arXiv2025

Detectability of the chiral gravitational wave background from audible axions with the LISA-Taiji network

The chiral gravitational wave background (GWB) can be produced by axion-like fields in the early universe. We perform parameter estimation for two types of chiral GWB with the LISA-Taiji network: axion-dark photon coupling and axion-Nieh-Yan coupling. We estimate the spectral parameters of these two mechanisms induced by axion and determine the normalized model parameters using the Fisher information matrix. For highly chiral GWB signals that we choose to analyze in the mHz band, the normalized model parameters are constrained with a relative error less than $6.7\%$ (dark photon coupling) and $2.2\%$ (Nieh-Yan coupling) at the one-sigma confidence level. The circular polarization parameters are constrained with a relative error around $21\%$ (dark photon coupling) and $6.2\%$ (Nieh-Yan coupling) at the one-sigma confidence level.

astro-ph.CO↗

arXiv2025

Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition

Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates contextual and semantic elements. In scenarios with degraded visual quality, linguistic patterns serve as crucial supplements for comprehension, highlighting the necessity of integrating both aspects for robust scene text recognition (STR). Contemporary STR approaches often use language models or semantic reasoning modules to capture linguistic features, typically requiring large-scale annotated datasets. Self-supervised learning, which lacks annotations, presents challenges in disentangling linguistic features related to the global context. Typically, sequence contrastive learning emphasizes the alignment of local features, while masked image modeling (MIM) tends to exploit local structures to reconstruct visual patterns, resulting in limited linguistic knowledge. In this paper, we propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch. Specifically, we design a linguistics alignment module to extract vision-independent features as linguistic guidance using inputs with different visual appearances. As features extend beyond mere visual structures, LMIM must consider the global context to achieve reconstruction. Extensive experiments on various benchmarks quantitatively demonstrate our state-of-the-art performance, and attention visualizations qualitatively show the simultaneous capture of both visual and linguistic information.

cs.CV↗

arXiv2025

Long Is More Important Than Difficult for Training Reasoning Models

Difficult problems, which often result in long reasoning traces, are widely recognized as key factors for enhancing the performance of reasoning models. However, such high-challenge problems are scarce, limiting the size of available datasets. In this paper, we propose a simple method to decouple the reliance on problem difficulty. First, we empirically demonstrate that reasoning length, rather than problem difficulty, primarily influences the performance of trained models. Second, we identify a scaling law on reasoning length, showing that model performance increases in a log-linear fashion as the reasoning data length grows. Finally, we introduce a straightforward technique to generate reasoning data of arbitrary length, and show that synthesized data is effective for training reasoning models. After fine-tuning the Qwen2.5-32B-Instruct language model on our Long1K dataset, we present our model, Long1K-32B, which achieves remarkable performance with only 1,000 training samples, achieving 95.6\% accuracy on MATH, and 71.1\% on GPQA outperforming DeepSeek-R1-Distill-Qwen-32B. The model, code, and dataset are all open-sourced, available at https://huggingface.co/ZTss/LONG1.

cs.CL↗