arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 91 records · Page 5Linked to original sources

arXiv2025

RWKV-PCSSC: Exploring RWKV Model for Point Cloud Semantic Scene Completion

Semantic Scene Completion (SSC) aims to generate a complete semantic scene from an incomplete input. Existing approaches often employ dense network architectures with a high parameter count, leading to increased model complexity and resource demands. To address these limitations, we propose RWKV-PCSSC, a lightweight point cloud semantic scene completion network inspired by the Receptance Weighted Key Value (RWKV) mechanism. Specifically, we introduce a RWKV Seed Generator (RWKV-SG) module that can aggregate features from a partial point cloud to produce a coarse point cloud with coarse features. Subsequently, the point-wise feature of the point cloud is progressively restored through multiple stages of the RWKV Point Deconvolution (RWKV-PD) modules. By leveraging a compact and efficient design, our method achieves a lightweight model representation. Experimental results demonstrate that RWKV-PCSSC reduces the parameter count by 4.18$\times$ and improves memory efficiency by 1.37$\times$ compared to state-of-the-art methods PointSSC. Furthermore, our network achieves state-of-the-art performance on established indoor (SSC-PC, NYUCAD-PC) and outdoor (PointSSC) scene dataset, as well as on our proposed datasets (NYUCAD-PC-V2, 3D-FRONT-PC).

cs.CV↗

arXiv2025

Probing Hidden Symmetry and Altermagnetism with Sub-Picometer Sensitivity via Nonlinear Transport

X-ray and neutron diffraction are foundational tools for determining crystal structures, but their resolution limits can lead to misassignments, especially in materials with subtle distortions or competing phases. Here, we demonstrate the use of nonlinear transport as a complementary approach to uncover hidden crystal symmetries, using the strongly correlated Ca$_3$Ru$_2$O$_7$ as a case study. Below 48 K (T$_S$), where the magnetic moments of the antiferromagnetic phase reorient from the a- to the b-axis, leading to a pseudogap opening, our measurements, with support of DFT, reveal a previously overlooked lower-symmetry phase. This is manifested by the emergence of longitudinal nonlinear resistance (NLR) along the b-axis below T$_S$, providing direct evidence of combined translational and time-reversal symmetry breaking. This response also suggests a transformation from a conventional antiferromagnet into an altermagnet. The lower-symmetry phase arises from a subtle lattice distortion (~0.1 pm) associated with the magnetic transition at T$_S$, below the detection limit of conventional diffraction. Moreover, this NLR below T$_S$ is accompanied by a nonlinear Hall effect, both of which are enhanced by the large quantum metric associated with Weyl chains near the Fermi surface. Our findings demonstrate nonlinear transport as a sensitive probe of hidden symmetry breaking and altermagnetism, complementing and extending beyond the reach of traditional diffraction and spectroscopic techniques.

cond-mat.str-el↗

arXiv2025

Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations

Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relative stability of stock relation structures while retaining the flexibility to respond to sudden market shifts. Our approach features a novel relative static relation component that models time-varying long-term patterns and incorporates overnight informational influences. We capture dynamic inter-stock relationships through distance-aware mechanisms, while evolving long-term structures via recurrent fusion of dynamic relations from the prior day with the pre-defined static relations. Experiments demonstrate that our method significantly outperforms the baselines across different markets, with high sensitivity to the co-movement of relational strength and stock price.

cs.LG↗

arXiv2025

Influence of Solar Sails on Magnetic Field Measurements in Space Plasmas

Solar sail technology is ready to be deployed in a satellite mission carrying a science-grade magnetometer. In preparation for such a mission, it is essential to characterize the interactions between the sail and the ambient plasma that could affect the magnetometer readings. The solar wind magnetic field is a key parameter in space weather prediction, because it governs the energy-releasing magnetic reconnection process at Earth's magnetopause. This paper investigates the influence of solar sails on the ambient magnetic field, particularly focusing on two critical electromagnetic effects: eddy currents and magnetic pileup. We find the induced eddy currents in the metallic sail can significantly perturb the local magnetic field at high frequencies. We also suggest that magnetic pileup can influence the spacecraft's environment when the sail size is comparable to the electron kinetic scales of the surrounding plasma. This research provides an initial guide for determining when sail-plasma interactions could impact magnetometer performance.

physics.space-ph↗

arXiv2025

Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMs

The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. While prior research has primarily examined their ability to generate Socratic questions, it often overlooks a critical aspect: adaptively guiding learners in accordance with their cognitive states. This study moves beyond question generation to emphasize instructional guidance capability. We ask: Can LLMs emulate expert tutors who dynamically adjust strategies in response to learners' states? To investigate this, we propose GuideEval, a benchmark grounded in authentic educational dialogues that evaluates pedagogical guidance through a three-phase behavioral framework: (1) Perception, inferring learner states; (2) Orchestration, adapting instructional strategies; and (3) Elicitation, stimulating proper reflections. Empirical results indicate that existing LLMs often fail to provide effective adaptive scaffolding when learners experience confusion or require redirection. To complement the quantitative evaluation, we conduct a detailed failure case analysis, providing an intuitive understanding of these shortcomings. Furthermore, we introduce a behavior-guided finetuning strategy that leverages behavior-prompted instructional dialogues, substantially enhancing guidance performance. By shifting the focus from isolated content evaluation to learner-centered state-aware interaction, our work advocates a more dialogic paradigm for evaluating Socratic LLMs.

cs.CL↗

arXiv2025

Layer controlled orbital selective Mott transition in monolayer nickelate

Dimensionality and electronic correlations are crucial elements of many quantum material properties. An example is the change of the electronic structure accompanied by the loss of quasiparticles when a metal is reduced from three dimensions to a lower dimension, where the Coulomb interaction between carriers becomes poorly screened. Here, using angle-resolved photoemission spectroscopy (ARPES), we report an orbital-selective decoherence of spectral density in the perovskite nickelate LaNiO3 towards the monolayer limit. The spectral weight of the dz2 band vanishes much faster than that of the dx2-y2 band as the thickness of the LaNiO3 layer is decreased to a single unit cell, indicating a stronger correlation effect for the former upon dimensional confinement. Dynamical mean-field theory (DMFT) calculations show an orbital-selective Mott transition largely due to the localization of dz2 electrons along the c axis in the monolayer limit. This orbital-selective correlation effect underpins many macroscopic properties of nickelates, such as metal-to-insulator transition and superconductivity, where most theories are built upon a dx2-y2-dz2 two-band model.

cond-mat.str-el↗

arXiv2025

Event-triggered robust control of linear systems: Sliding mode cone method

In this paper, we investigate the global robust stabilization of linear time-invariant systems by using event-triggered sliding mode control (SMC). Different from the practical sliding mode band, which is commonly used in previous studies on event-triggered SMC, a new concept of ideal sliding mode cone is proposed in this paper. Specifically, we design a hybrid event-triggering mechanism that takes into account both the size and direction shift of the error state. The proposed event-triggered SMC law is shown to enforce and sustain the system state in the ideal sliding mode cone. Moreover, the state of the closed-loop system can asymptotically converge to the equilibrium point, rather than merely to a neighborhood of it, which is usually difficult to handle by using practical sliding mode band. Technically speaking, to achieve strong convergence, the triggering frequency should naturally be as high as possible due to the existence of the external disturbances, but this will also increase the communication load. Hence, to balance the asymptotic convergence and frequent triggering near the equilibrium point that is the price paid for achieving asymptotic stability, we extend the obtained results to the case of practical sliding mode cone. In addition, it is verified that the ETM is global, namely, the inter-event times are uniformly lower bounded from zero globally. Further, a practical application for the quadrotor unmanned aerial vehicles is presented. Finally, three illustrative examples are given to demonstrate the effectiveness of the obtained results.

math.OC↗

arXiv2025

Citation Issues in Wave Mechanics Theory of Microwave Absorption

The wave mechanics theory of microwave absorption challenges the long-standing impedance-matching and quarter-wavelength paradigms by demonstrating that conventional models mistakenly conflate bulk material parameters with thin-film phenomena. Drawing on a corpus of 35 peer-reviewed papers and preprints, the study performs a citation-pattern analysis and a logical audit of established theory. Results reveal a striking asymmetry in scholarly engagement, only a handful of supportive or neutral citations appear amid widespread silence, alongside critical logical flaws in impedance matching, notably its inconsistent treatment of penetration, reflection, and absorption from film. By re-framing absorption as a wave-mechanics process governed by interference at parallel interfaces, the wave mechanics framework restores energy-conservation consistency and provides experimentally verified design rules for film thickness, phase response, and broadband performance. The paper further situates the citation neglect within broader issues of peer-review bias and paradigm inertia, illustrating how cargo-cult scientific practices can impede theoretical progress. Recommendations are offered for researchers, editors, and institutions to foster open discourse, rigorously test competing models, and update curricula and design tools accordingly.

physics.soc-ph↗

arXiv2025

Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System

When combining Large Language Models (LLMs) with autonomous agents, used in network monitoring and decision-making systems, this will create serious security issues. In this research, the MAESTRO framework consisting of the seven layers threat modeling architecture in the system was used to expose, evaluate, and eliminate vulnerabilities of agentic AI. The prototype agent system was constructed and implemented, using Python, LangChain, and telemetry in WebSockets, and deployed with inference, memory, parameter tuning, and anomaly detection modules. Two practical threat cases were confirmed as follows: (i) resource denial of service by traffic replay denial-of-service, and (ii) memory poisoning by tampering with the historical log file maintained by the agent. These situations resulted in measurable levels of performance degradation, i.e. telemetry updates were delayed, and computational loads were increased, as a result of poor system adaptations. It was suggested to use a multilayered defense-in-depth approach with memory isolation, validation of planners and anomaly response systems in real-time. These findings verify that MAESTRO is viable in operational threat mapping, prospective risk scoring, and the basis of the resilient system design. The authors bring attention to the importance of the enforcement of memory integrity, paying attention to the adaptation logic monitoring, and cross-layer communication protection that guarantee the agentic AI reliability in adversarial settings.

cs.CR↗

arXiv2025

NetMoniAI: An Agentic AI Framework for Network Security & Monitoring

In this paper, we present NetMoniAI, an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination. The framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection. A central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness. We evaluated NetMoniAI on a local micro-testbed and through NS-3 simulations. Results confirm that the two-tier agentic-AI design scales under resource constraints, reduces redundancy, and improves response time without compromising accuracy. To facilitate broader adoption and reproducibility, the complete framework is available as open source. This enables researchers and practitioners to replicate, validate, and extend it across diverse network environments and threat scenarios. Github link: https://github.com/pzambare3/NetMoniAI

cs.CR↗

arXiv2025

Observation of edge solitons and transitions between them in a trimer circuit lattice

In nonlinear topological systems, edge solitons either originate from linear topological edge modes or emerge as nonlinearity-induced localized states without topological protection. While electric circuits (ECs) provide a platform for realizing various types of topological insulators, observation of edge solitons and transitions between them in EC lattices remains a challenging problem. Here, we realize quench dynamics in nonlinear ECs to experimentally demonstrate both topologically nontrivial and trivial edge solitons in a trimer EC lattice and transitions between them. In the weakly nonlinear regime, we observe two types of topologically nontrivial edge solitons that originate from the corresponding linear topological edge states, characterized by the presence of mutually antisymmetric or symmetric peaks at two edge sites. Under strong nonlinearity, topologically trivial edge solitons with antisymmetric, symmetric, and asymmetric internal structures are discovered. The work suggests possibilities for exploring sophisticated nonlinear states and transitions between them in nonlinear topological systems.

nlin.PS↗

arXiv2025

Seeing Beyond Frames: Zero-Shot Pedestrian Intention Prediction with Raw Temporal Video and Multimodal Cues

Pedestrian intention prediction is essential for autonomous driving in complex urban environments. Conventional approaches depend on supervised learning over frame sequences and require extensive retraining to adapt to new scenarios. Here, we introduce BF-PIP (Beyond Frames Pedestrian Intention Prediction), a zero-shot approach built upon Gemini 2.5 Pro. It infers crossing intentions directly from short, continuous video clips enriched with structured JAAD metadata. In contrast to GPT-4V based methods that operate on discrete frames, BF-PIP processes uninterrupted temporal clips. It also incorporates bounding-box annotations and ego-vehicle speed via specialized multimodal prompts. Without any additional training, BF-PIP achieves 73% prediction accuracy, outperforming a GPT-4V baseline by 18 %. These findings illustrate that combining temporal video inputs with contextual cues enhances spatiotemporal perception and improves intent inference under ambiguous conditions. This approach paves the way for agile, retraining-free perception module in intelligent transportation system.

cs.CV↗

arXiv2025

Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles

The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach.

cs.RO↗

arXiv2025

Team PA-VCG's Solution for Competition on Understanding Chinese College Entrance Exam Papers in ICDAR'25

This report presents Team PA-VGG's solution for the ICDAR'25 Competition on Understanding Chinese College Entrance Exam Papers. In addition to leveraging high-resolution image processing and a multi-image end-to-end input strategy to address the challenges of dense OCR extraction and complex document layouts in Gaokao papers, our approach introduces domain-specific post-training strategies. Experimental results demonstrate that our post-training approach achieves the most outstanding performance, securing first place with an accuracy rate of 89.6%.

cs.CV↗

arXiv2025

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions

Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system maintenance, which are essential to our data center reliability and optimizations, and are not explored in prior work. We show repredictions deliver a fundamental advance in effectiveness over one-shot prediction. We call our novel combination of distribution-based lifetime predictions and scheduling algorithms Lifetime Aware VM Allocation (LAVA). LAVA reduces resource stranding and increases the number of empty hosts, which are critical for large VM scheduling, cloud system updates, and reducing dynamic energy consumption. Our approach runs in production within Google's hyperscale cloud data centers, where it improves efficiency by decreasing stranded compute and memory resources by ~3% and ~2% respectively. It increases empty hosts by 2.3-9.2 pp in production, reducing dynamic energy consumption, and increasing availability for large VMs and cloud system updates. We also show a reduction in VM migrations for host defragmentation and maintenance. In addition to our fleet-wide production deployment, we perform simulation studies to characterize the design space and show that our algorithm significantly outperforms the prior state of the art lifetime-based scheduling approach.

cs.DC↗

arXiv2025

Enhancing the sensitivity of quantum optomechanical gyroscope by optical Kerr effect

We propose a theoretical scheme to enhance the sensitivity of a quantum optomechanical gyroscope (QOMG) by optical Kerr effect. We utilize quantum Fisher information (QFI) to evaluate the metrological potential of the QOMG scheme. It is found that the Kerr interaction can significantly enhances the sensitivity of the QOMG. We observe the super-Hesenberg scaling of parameter estimation precision. Furthermore, we also evaluate the performance of QOMG for the quadrature measurement. It is indicated that the sensitivity in the quadrature measurement scheme can saturate the quantum Crmamér-Rao bound. We study the effect of the driving and dissipation of the optical cavity on the QFI, and find that the sensitivity can be manipulated by changing the driving while dissipation decreases the sensitivity. The work shows that the photon nonlinear interaction can improve sensitivity of QOMG, and demonstrates a valuable quantum resource for the QOMG. These results could have a wide-ranging impact on developing high-performance QOMG in the future.

quant-ph↗

arXiv2025

From the New World of Word Embeddings: A Comparative Study of Small-World Lexico-Semantic Networks in LLMs

Lexico-semantic networks represent words as nodes and their semantic relatedness as edges. While such networks are traditionally constructed using embeddings from encoder-based models or static vectors, embeddings from decoder-only large language models (LLMs) remain underexplored. Unlike encoder models, LLMs are trained with a next-token prediction objective, which does not directly encode the meaning of the current token. In this paper, we construct lexico-semantic networks from the input embeddings of LLMs with varying parameter scales and conduct a comparative analysis of their global and local structures. Our results show that these networks exhibit small-world properties, characterized by high clustering and short path lengths. Moreover, larger LLMs yield more intricate networks with less small-world effects and longer paths, reflecting richer semantic structures and relations. We further validate our approach through analyses of common conceptual pairs, structured lexical relations derived from WordNet, and a cross-lingual semantic network for qualitative words.

cs.CL↗

arXiv2025

MROSS: Multi-Round Region-based Optimization for Scene Sketching

Scene sketching is to convert a scene into a simplified, abstract representation that captures the essential elements and composition of the original scene. It requires a semantic understanding of the scene and consideration of different regions within the scene. Since scenes often contain diverse visual information across various regions, such as foreground objects, background elements, and spatial divisions, dealing with these different regions poses unique difficulties. In this paper, we define a sketch as some sets of Bézier curves because of their smooth and versatile characteristics. We optimize different regions of input scene in multiple rounds. In each optimization round, the strokes sampled from the next region can seamlessly be integrated into the sketch generated in the previous optimization round. We propose an additional stroke initialization method to ensure the integrity of the scene and the convergence of optimization. A novel CLIP-based Semantic Loss and a VGG-based Feature Loss are utilized to guide our multi-round optimization. Extensive experimental results on the quality and quantity of the generated sketches confirm the effectiveness of our method.

cs.CV↗