arXiv subjects

Liang Chen

Publications and source records attributed to Liang Chen.

At least 145 records · Page 8Linked to original sources

arXiv2025

Jet reorientation revealed by intermittent jet activity in radio galaxy 0954+556

Intermittent jet activity of AGNs is a common phenomenon, whereas significant jet reorientation during episodic jet activity in relatively young radio galaxies are rarely reported. The quasar 0954+556 at redshift of 0.903 is an intriguing source exhibiting an unusual radio jet structure with significantly different jet directions at kpc and pc scales. At kpc scales, images from the VLA exhibit a bright core, a linear jet extending 24 kpc to the northwest, and a discrete jet component 16 kpc to the northeast. At pc scales, images from the VLBA show a two-component structure with a projected separation of 360 pc in the north-south direction. The peculiar structure of 0954+556 might result from jet reorientation. Here, our aim was to investigate the possible mechanism via multiscale and multifrequency deep radio images. We performed VLA and VLBA observations of 0954+556. Together with some existing data in the NRAO data archive, we made multiple VLA images at 1.4-22 GHz and VLBA images at 1.7-43 GHz for various image analyses of the jet structure. We identified the location of the radio core at pc scales, detected the faint counter-jets at both pc and kpc scales for the first time, and revealed a diffuse emission region connecting pc and kpc scale forward jets. Our spectral index distribution and spectral aging analysis indicate that 0954+556 might undergo at least two episodes of jet activity during the current AGN phase. Moreover, pc scale polarization maps display a well-resolved spine-sheath polarization structure. It seems that the jet direction of 0954+556 changed significantly during intermittent jet activity. This may explain the different jet orientations and spectral ages observed from kpc to pc scales. The research provides a strong case that AGN jet direction might change rapidly on timescales of one million years.

astro-ph.HE↗

arXiv2025

A Convergent Inexact Abedin-Kitagawa Iteration Method for Monge-Ampère Eigenvalue Problems

This paper proposes an inexact Aleksandrov-solution-based iteration method, formulated by adapting the convergent Rayleigh inverse iterative scheme introduced by Abedin and Kitagawa, to solve real Monge-Amp{è}re eigenvalue (MAE) problems. The central feature of the proposed approach is the introduction of a flexible error tolerance criterion for computing inexact Aleksandrov solutions to the required subproblems. This allows the inner iteration to be solved approximately without compromising the global convergence properties of the overall scheme, as we established under a ${\cal C}^{2,α}$ boundary condition, and has the potential of achieving reduced computational cost compared to the original algorithm. In practice, for both two- and three-dimensional problems, by leveraging the flexibility of the inexact iterative formulation in conjunction with a fixed-point approach for solving the subproblems, the proposed method performs several times faster than its original version of Abedin and Kitagawa, across all tested problem instances in the numerical experiments.

math.NA↗

arXiv2025

All-sky search for individual Primordial Black Hole bursts with LHAASO

Primordial Black Holes~(PBHs) are hypothetical black holes with a wide range of masses that formed in the early universe. As a result, they may play an important cosmological role and provide a unique probe of the early universe. A PBH with an initial mass of approximately $10^{15}$~g is expected to explode today in a final burst of Hawking radiation. In this work, we conduct an all-sky search for individual PBH burst events using the data collected from March 2021 to July 2024 by the Water Cherenkov Detector Array of the Large High Altitude Air Shower Observatory (LHAASO). Three PBH burst durations, 10~s, 20~s, and 100~s, are searched, with no significant PBH bursts observed. The upper limit on the local PBH burst rate density is set to be as low as 181~pc$^{-3}$~yr$^{-1}$ at 99$\%$ confidence level, representing the most stringent limit achieved to date.

astro-ph.HE↗

arXiv2025

Behavior of Gauss curvatures and mean curvatures of Lightcone framed surfaces in the Lorentz-Minkowski 3-space

In this paper, we investigate the differential geometric properties of lightcone framed surfaces in Lorentz-Minkowski 3-space. In general, a mixed type surface is a connected regular surface with non-empty spacelike and timelike point sets. While a lightcone framed surface is a mixed type surface with singular points at least locally. We introduce a useful tool, so called modified frame along the lightcone framed surface, to study the differential geometric properties of the lightcone framed surface. As results, we show the behavior of the Gaussian curvature and mean curvature of the lightcone framed surface at not only lightlike points but also singular points.

math.DG↗

arXiv2025

Finite temperature Casimir effect in one-dimensional scalar field with double delta-function potentials

We investigate the finite-temperature Casimir effect for a (1+1)-dimensional scalar field interacting with a pair of delta-function potentials. We employ the canonical quantization method to compute the Casimir force and entropy, contrasting the results with those from the standard Lifshitz theory. At zero temperature, both frameworks yield identical forces. For the finite-temperature case, we find that in the long-distance limit, the Casimir force decays asymptotically as $F_C(a,T)=-T/(4a)$, with the Lifshitz theory predicting a magnitude twice as large as that from canonical quantization. Crucially, the canonical quantization method yields a physically consistent entropy that remains positive and increases with temperature. These results demonstrate the robustness of the canonical quantization approach in providing a thermodynamically sound description of the thermal Casimir effect in this system.

quant-ph↗

arXiv2025

A Giant Peanut-shaped Ultra-High-Energy Gamma-Ray Emitter Off the Galactic Plane

Ultra-high-energy (UHE), exceeding 100 TeV (10^12 electronvolts), γ-rays manifests extreme particle acceleration in astrophysical sources. Recent observations by γ-ray telescopes, particularly by the Large High Altitude Air Shower Observatory (LHAASO), have revealed a few tens of UHE sources, indicating numerous Galactic sources capable of accelerating particles to PeV (10^15 electronvolts) energies. However, discerning the dominant acceleration mechanisms (leptonic versus hadronic), the relative contributions of specific source classes, and the role of particle transport in shaping their observed emission are central goals of modern UHE astrophysics. Here we report the discovery of a giant UHE γ-ray emitter at -17.5° off the Galactic plane - a region where UHE γ-ray sources are rarely found. The emitter exhibits a distinctive asymmetric shape, resembling a giant "Peanut" spanning 0.45° \times 4.6°, indicative of anisotropic particle distribution over a large area. A highly aged millisecond pulsar (MSP) J0218+4232 is the sole candidate accelerator positionally coincident with the Peanut region. Its association with UHE γ-rays extending to 0.7 PeV, if confirmed, would provide the first evidence of a millisecond pulsar powering PeV particles. Such a finding challenges prevailing models, which posit that millisecond pulsars cannot sustain acceleration to PeV energies. The detection reveals fundamental gaps in understanding particle acceleration, cosmic-ray transport, and interstellar magnetic field effects, potentially revealing new PeV accelerator (PeVatron) classes.

astro-ph.HE↗

arXiv2025

An Ensembled Penalized Federated Learning Framework for Falling People Detection

Falls among elderly and disabled individuals remain a leading cause of injury and mortality worldwide, necessitating robust, accurate, and privacy-aware fall detection systems. Traditional fall detection approaches, whether centralized or point-wise, often struggle with key challenges such as limited generalizability, data privacy concerns, and variability in individual movement behaviors. To address these limitations, we propose EPFL-an Ensembled Penalized Federated Learning framework that integrates continual learning, personalized modeling, and a novel Specialized Weighted Aggregation (SWA) strategy. EPFL leverages wearable sensor data to capture sequential motion patterns while preserving user privacy through homomorphic encryption and federated training. Unlike existing federated models, EPFL incorporates both penalized local training and ensemble-based inference to improve inter-client consistency and adaptability to behavioral differences. Extensive experiments on a benchmark fall detection dataset demonstrate the effectiveness of our approach, achieving a Recall of 88.31 percent and an F1-score of 89.94 percent, significantly outperforming both centralized and baseline models. This work presents a scalable, secure, and accurate solution for real-world fall detection in healthcare settings, with strong potential for continuous improvement via its adaptive feedback mechanism.

cs.LG↗

arXiv2025

Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation

We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and over half touch more than four tables. Each example is annotated along SQL-computation features and Chinese semantics. For evaluation, we release a robust execution comparator and an automated evaluation pipeline, under which all current state-of-the-art large-scale models (including Deepseek) achieve accuracies of at most 50%. Major errors originate from two sources: (1) schema linking in large enterprise landscapes - hundreds of tables, denormalized fields, ambiguous column names, implicit foreign-key relations and domain-specific synonyms that make correct join/column selection difficult; and (2) mapping concise, colloquial Chinese into the exact operators and predicates required for analytics - e.g., choosing the correct aggregation and group-by keys, expressing time windows and granularities, applying unit conversions, handling NULLs and data-quality rules, and formulating nested or windowed subqueries. Falcon therefore targets Chinese-specific semantics and enterprise dialects (abbreviations, business jargon, fuzzy entity references) and provides a reproducible middle ground before full production deployment by using realistic enterprise schemas, query templates, an execution comparator, and an automated evaluation pipeline for end-to-end validation.

cs.CL↗

arXiv2025

Line-force driven wind from a thin disk in tidal disruption event

Winds from the accretion disk in tidal disruption events (TDEs) play a key role in determining the radiation of TDEs. The winds from the super-Eddington accretion phase in TDEs have recently been studied. However, properties of the winds from the sub-Eddington accretion disk in TDEs are not clear. We aim to investigate properties of winds from the circularized sub-Eddington accretion disk in TDEs. We study the line force driven accretion disk wind. We perform two-dimensional hydrodynamic simulations using the PLUTO code to study the line force driven wind from the circularized accretion disk around a $10^6$ solar mass black hole in TDEs. We find that although the disk has a very small size in TDEs, strong wind can be driven by line force when the disk have luminosity higher than $20\%$ of the Eddington luminosity. The maximum velocity of wind can be as high as $0.3$ times the speed of light. The kinematic power of wind is in the range of $1\%-6\%$ times the Eddington luminosity. Strong wind can be driven by line force from the thin disk around a $10^6$ solar mass black hole in TDEs. We briefly discuss the possible radio emission from the shock when the wind collides with the surrounding medium.

astro-ph.HE↗

arXiv2025

Dynamic Lasing of Axion Clusters

We examine high-density axion clusters under gravitational compression. These are transient events in which the majority of axions are rapidly converted into photons, with some configurations producing photon signals with distinctive and characteristic patterns. We estimated the mass of the remnant objects and note that some could be black holes while in some cases it may be possible to identify the emitted photons with a robust class of fast radio bursts.

hep-ph↗

arXiv2025

Mitigating Overthinking through Reasoning Shaping

Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier Reward (RLVR) have shown great power in problem solving, yet they often cause overthinking: excessive, meandering reasoning that inflates computational cost. Prior designs of penalization in RLVR manage to reduce token consumption while often harming model performance, which arises from the oversimplicity of token-level supervision. In this paper, we argue that the granularity of supervision plays a crucial role in balancing efficiency and accuracy, and propose Group Relative Segment Penalization (GRSP), a step-level method to regularize reasoning. Since preliminary analyses show that reasoning segments are strongly correlated with token consumption and model performance, we design a length-aware weighting mechanism across segment clusters. Extensive experiments demonstrate that GRSP achieves superior token efficiency without heavily compromising accuracy, especially the advantages with harder problems. Moreover, GRSP stabilizes RL training and scales effectively across model sizes.

cs.CL↗

arXiv2025

SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex Multimodal Reasoning in Academic Areas

Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with complex images in academic domains has not been systematically investigated. To bridge this gap, we present SCI-Reason, a dataset for complex multimodel reasoning in academic areas. SCI-Reason aims to test and improve the reasoning ability of large multimodal models using real complex images in academic domains. The dataset contains 12,066 images and 12,626 question-answer pairs extracted from PubMed, divided into training, validation and test splits. Each question-answer pair also contains an accurate and efficient inference chain as a guide to improving the inference properties of the dataset. With SCI-Reason, we performed a comprehensive evaluation of 8 well-known models. The best performing model, Claude-3.7-Sonnet, only achieved an accuracy of 55.19%. Error analysis shows that more than half of the model failures are due to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding underscores the inherent limitations in reasoning capabilities exhibited by current multimodal models when processing complex image analysis tasks within authentic academic contexts. Experiments on open-source models show that SCI-Reason not only enhances reasoning ability but also demonstrates cross-domain generalization in VQA tasks. We also explore future applications of model inference capabilities in this domain, highlighting its potential for future research.

cs.MM↗

arXiv2025

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph

Graph Neural Networks (GNNs) are exemplary deep models designed for graph data. Message passing mechanism enables GNNs to effectively capture graph topology and push the performance boundaries across various graph tasks. However, the trend of developing such complex machinery for graph representation learning has become unsustainable on large-scale graphs. The computational and time overhead make it imperative to develop more energy-efficient GNNs to cope with the explosive growth of real-world graphs. Spiking Graph Neural Networks (SGNNs), which integrate biologically plausible learning via unique spike-based neurons, have emerged as a promising energy-efficient alternative. Different layers communicate with sparse and binary spikes, which facilitates computation and storage of intermediate graph representations. Despite the proliferation of SGNNs proposed in recent years, there is no systematic benchmark to explore the basic design principles of these brain-inspired networks on the graph data. To bridge this gap, we present SGNNBench to quantify progress in the field of SGNNs. Specifically, SGNNBench conducts an in-depth investigation of SGNNs from multiple perspectives, including effectiveness, energy efficiency, and architectural design. We comprehensively evaluate 9 state-of-the-art SGNNs across 18 datasets. Regarding efficiency, we empirically compare these baselines w.r.t model size, memory usage, and theoretical energy consumption to reveal the often-overlooked energy bottlenecks of SGNNs. Besides, we elaborately investigate the design space of SGNNs to promote the development of a general SGNN paradigm.

cs.NE↗

arXiv2025

Observatory Science with eXTP

Scheduled for launch in 2030, the enhanced X-ray Timing and Polarization (eXTP) telescope is a Chinese space-based mission aimed at studying extreme conditions and phenomena in astrophysics. eXTP will feature three main payloads: Spectroscopy Focusing Arrays (SFAs), Polarimetry Focusing Arrays (PFAs), and a Wide-field Camera (W2C). This white paper outlines observatory science, incorporating key scientific advances and instrumental changes since the publication of the previous white paper [1]. We will discuss perspectives of eXTP on the research domains of flare stars, supernova remnants, pulsar wind nebulae, cataclysmic variables, X-ray binaries, ultraluminous X-ray sources, AGN, and pulsar-based positioning and timekeeping.

astro-ph.IM↗

arXiv2025

VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection

Unsupervised domain adaptation methods have been widely explored to bridge domain gaps. However, in real-world remote-sensing scenarios, privacy and transmission constraints often preclude access to source domain data, which limits their practical applicability. Recently, Source-Free Object Detection (SFOD) has emerged as a promising alternative, aiming at cross-domain adaptation without relying on source data, primarily through a self-training paradigm. Despite its potential, SFOD frequently suffers from training collapse caused by noisy pseudo-labels, especially in remote sensing imagery with dense objects and complex backgrounds. Considering that limited target domain annotations are often feasible in practice, we propose a Vision foundation-Guided DEtection TRansformer (VG-DETR), built upon a semi-supervised framework for SFOD in remote sensing images. VG-DETR integrates a Vision Foundation Model (VFM) into the training pipeline in a "free lunch" manner, leveraging a small amount of labeled target data to mitigate pseudo-label noise while improving the detector's feature-extraction capability. Specifically, we introduce a VFM-guided pseudo-label mining strategy that leverages the VFM's semantic priors to further assess the reliability of the generated pseudo-labels. By recovering potentially correct predictions from low-confidence outputs, our strategy improves pseudo-label quality and quantity. In addition, a dual-level VFM-guided alignment method is proposed, which aligns detector features with VFM embeddings at both the instance and image levels. Through contrastive learning among fine-grained prototypes and similarity matching between feature maps, this dual-level alignment further enhances the robustness of feature representations against domain gaps. Extensive experiments demonstrate that VG-DETR achieves superior performance in source-free remote sensing detection tasks.

cs.CV↗

arXiv2025

TaiBai: A fully programmable brain-inspired processor with topology-aware efficiency

Brain-inspired computing has emerged as a promising paradigm to overcome the energy-efficiency limitations of conventional intelligent systems by emulating the brain's partitioned architecture and event-driven sparse computation. However, existing brain-inspired chips often suffer from rigid network topology constraints and limited neuronal programmability, hindering their adaptability. To address these challenges, we present TaiBai, an event-driven, programmable many-core brain-inspired processor that leverages temporal and spatial spike sparsity to minimize bandwidth and computational overhead. TaiBai chip contains three key features: First, a brain-inspired hierarchical topology encoding scheme is designed to flexibly support arbitrary network architectures while slashing storage overhead for large-scale networks; Second, a multi-granularity instruction set enables programmability of brain-like spiking neuron or synapses with various dynamics and on-chip learning rules; Third, a co-designed compiler stack optimizes task mapping and resource allocation. After evaluating across various tasks, such as speech recognition, ECG classification, and cross-day brain-computer interface decoding, we found spiking neural networks embedded on the TaiBai chip could achieve more than 200 times higher energy efficiency than a standard NVIDIA RTX 3090 GPU at a comparable accuracy. These results demonstrated its high potentiation as a scalable, programmable, and ultra-efficient solution for both multi-scale brain simulation and brain-inspired computation.

cs.AR↗

arXiv2025

A Law of Emergence: Maximum Causal Power at the Mesoscale

Complex systems universally exhibit emergence, where macroscopic dynamics arise from local interactions, but a predictive law governing this process has been absent. We establish and verify such a law. We define a system's causal power at a spatial scale, $\ell$, as its Effective Information (EI$_\ell$), measured by the mutual information between a targeted, maximum-entropy intervention and its outcome. From this, we derive and prove a Middle-Scale Peak Theorem: for a broad class of systems with local interactions, EI$_\ell$ is not monotonic but exhibits a strict maximum at a mesoscopic scale $\ell^*$. This peak is a necessary consequence of a fundamental trade-off between noise-averaging at small scales and locality-limited response at large scales. We provide quantitative, reproducible evidence for this law in two distinct domains: a 2D Ising model near criticality and a model of agent-based collective behavior. In both systems, the predicted unimodal peak is decisively confirmed by statistical model selection. Our work establishes a falsifiable, first-principles law that identifies the natural scale of emergence, providing a quantitative foundation for the discovery of effective theories.

cs.IT↗

arXiv2025

UI-Venus Technical Report: Building High-performance UI Agents with RFT

We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous SOTA baselines including open-source GTA1 and closed-source UI-TARS-1.5. To show UI-Venus's summary and planing ability, we also evaluate it on the AndroidWorld, an online UI navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9% success rate, also beating existing models. To achieve this, we introduce carefully designed reward functions for both UI grounding and navigation tasks and corresponding efficient data cleaning strategies. To further boost navigation performance, we propose Self-Evolving Trajectory History Alignment & Sparse Action Enhancement that refine historical reasoning traces and balances the distribution of sparse but critical actions, leading to more coherent planning and better generalization in complex UI tasks. Our contributions include the publish of SOTA open-source UI agents, comprehensive data cleaning protocols and a novel self-evolving framework for improving navigation performance, which encourage further research and development in the community. Code is available at https://github.com/inclusionAI/UI-Venus.

cs.CV↗