arXiv subjects

Yihan Liu

Publications and source records attributed to Yihan Liu.

At least 19 recordsLinked to original sources

arXiv2026

Reconstruction of Shower-like Events in NEON Using Likelihood and Graph Neural Network Methods

The Neutrino Observatory in the Nanhai (NEON) is a proposed deep-sea neutrino telescope deployed in the South China Sea. Accurate reconstruction of shower-like events is crucial for neutrino energy measurements and multi-messenger astronomy, yet it poses significant challenges due to seawater optical attenuation, irregular detector geometry, and substantial $^{40}\mathrm{K}$ ambient background. In this work, we present the first comprehensive reconstruction framework for shower-like events in NEON, encompassing both a physics-driven maximum likelihood estimation (MLE) method and a data-driven Graph Neural Network (GNN). The traditional MLE framework integrates spatial-isochronic hit selection, vertex reconstruction via time-residual M-estimator minimization, and decoupled directional and energy estimation based on pre-computed photon distribution tables. Physical calibrations, including PMT angular acceptance, hit-level time slewing corrections, and an effective line-source shower extension, are incorporated into the likelihood formulation. In parallel, a two-stage GNN is developed to capture intra-DOM PMT correlations and distance-weighted inter-DOM topological patterns. Simulation studies show that the MLE method achieves an overall median angular resolution of $4.19^\circ$ and an energy resolution of 25\%-37\% over 1 TeV to 1 PeV with negligible systematic bias. The GNN further improves reconstruction fidelity in the low-to-intermediate energy regime, achieving a median angular resolution of $1.8^\circ$ at 30 TeV and an energy resolution of $\sim$ 20\% between 40 and 300 TeV. Based on these reconstruction performances, the effective area and point-source discovery potential of NEON are evaluated. This framework establishes an essential reconstruction benchmark for NEON and provides practical methodologies for future next-generation deep-sea neutrino telescopes.

astro-ph.IM↗

arXiv2026

Rapid Mapping of Photocathode Quantum Efficiency: A Magnetized Electron Beam Imaging Approach

Quantum efficiency (QE) is a key property of photocathodes, and its uniformity is essential for producing high-brightness electron beams. Cathode imaging provides an \textit{in-situ} and real-time approach for QE mapping, but in RF guns, a high charge per bunch is often needed to obtain a sufficient signal-to-noise ratio. Under such conditions, space charge effects can significantly degrade the imaging resolution and may even make point-to-point cathode imaging ineffective. In this paper, we propose a novel cathode imaging method based on a magnetized electron beam. Its feasibility is examined through theoretical analysis and beam dynamics simulations. The results show that the proposed method enables point-to-point cathode imaging in the ten picocoulomb charge regime. For a 10 pC, 3ps beam with a cathode magnetic field of 1200 Gauss, simulations indicate an imaging resolution of 11 um, representing nearly an order-of-magnitude improvement over the non-magnetized beam method.

physics.acc-ph↗

arXiv2026

A Responsible Artificial Intelligence Framework for Groundwater Modeling

The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.

cs.AI↗

arXiv2026

Risk assessment of muon single-event effects for low-altitude aircraft

With the rapid development of low-altitude economy, the radiation environment safety of low-altitude aircraft such as drones and electric vertical take-off and landing aircraft has attracted increasing attention. Although the dense lower atmosphere traditionally serves as an effective shield against cosmic radiation, the shrinking feature sizes of modern integrated circuits greatly enhance their vulnerability to single-event effects (SEEs). This study quantitatively evaluates muon-induced SEE risks for low-altitude aircraft in various regions of China under both static cosmic-ray background and ground-level enhancement (GLE) events, aiming to provide critical guidance for the next-generation low-altitude aviation platforms.Using city-specific atmospheric models within the CORSIKA framework, we simulate atmospheric shower processes and obtain reliable energy spectra for low-energy muons (10-100 MeV). We also employ simulation data from other research groups to estimate muon-induced SEE cross sections for transistors at different process nodes, including bulk, FD-SOI, and FinFET technologies. By incorporating solar energetic particle spectra associated with GLE events, we assess muon-induced SEE risks under both static and GLE conditions. Our results show that under static conditions, flight control systems with 1 MB memory using advanced nodes below 45 nm and bulk transistors face non-negligible muon-induced SEE risks in all Chinese cities. In contrast, systems with FD-SOI transistors can effectively mitigate these risks. For large-memory systems (1 GB), redundancy or other hardening measures are essential regardless of the process technology. Regarding GLE events, we introduce the concept of muon hazard levels to evaluate regional risk variations. During GLEs, the increase in muon-induced SEE risk is negligible in mid-to-low latitude regions but becomes significant at high latitudes.

astro-ph.IM↗

arXiv2026

Data-driven modeling of Galactic diffuse emission with multi-wavelength observations

We present a data-driven investigation of Galactic diffuse emission. Using multi-frequency Planck radio/microwave maps (30-857 GHz) and Fermi-LAT gamma-ray data (50 MeV-814 GeV), we construct a nonlinear mapping between radio emission and gamma-ray intensity through supervised machine learning. Our models achieve high predictive accuracy (R^2 > 0.90 in the 0.1-10 GeV range), demonstrating that multi-frequency radio observations encode sufficient information to reconstruct both spatial morphology and spectral properties of diffuse gamma-ray emission. By analyzing model performance across different frequency bands and spatial regions, we identify high-frequency radio bands as the dominant predictor, providing direct empirical support for the hadronic origin of Galactic 0.1-10 GeV gamma rays, while low-frequency radio bands for the leptonic origin above 10 GeV. Residual maps reveal coherent large-scale structures, including Loop I and III, highlighting regions where standard interstellar emission models are incomplete or biased. Compared with the GALPROP model, our machine learning approach yields a higher R^2=0.95 and lower mean absolute relative error (14.7%) in the inner Galactic disk and the Galactic center region. Our results illustrate that machine learning serves as a physically interpretable tool for multi-messenger astrophysics, providing a data-driven baseline for separating non-standard emission components and deriving new constraints on cosmic-ray propagation and interstellar medium structure.

astro-ph.HE↗

arXiv2026

Radio Study of G76.9+1.0 Pulsar Wind Nebula

Pulsar Wind Nebulae (PWNe) are key astrophysical laboratories for high energy phenomena. Specifically, radio observations and related polarimetry are essential probes to understand acceleration and transport, as well as PWN interaction with environment. We aim to better study the multi-wavelength morphology and magnetic geometry of \gname\ PWN (a system between early and middle ages). We conduct high resolution VLA observations at 3 cm (X band), 6 cm (C band), and 13 cm (S band) and compare them with the archival Chandra X-ray data. We also performed spectral analysis and radio polarimetry based on our radio observations. Our new VLA observations reveal a north-south double-lobed PWN bracketing a bridge-like feature, with the pulsar clearly resolved at C and S bands. The polarization fraction reaches 30\% across all bands, with the bridge region showing ordered north-south magnetic fields aligned with the X-ray torus elongation, while the southern outer lobe exhibits fields not following such a direction and the northern lobe displays a more chaotic configuration. Notably, we detect a significant radio-X-ray anti-correlation near the pulsar, with bright radio emission appearing just beyond the compact X-ray PWN boundary, multiwavelength spectral analysis suggest distinct particle populations. The radio PWN spectral index steepens from $\alpha\sim-0.3$ in the inner bridge to $<-1.0$ in the outer lobes, yet we suggest it is less likely related to synchrotron cooling. We tried to use a thick torus model with toroidal $B$-field to reproduce observed features; the result implies possible particle deceleration in the radio PWN. The equipartition magnetic field strength is estimated to be $\sim$15.3\,$\mu$G.

astro-ph.HE↗

arXiv2026

Lure-and-Reveal: An Exposure Framework for Stealthy Deception Attack in Multi-sensor Uncertain Systems

Multi-sensor integration via error-state Kalman filter (KF) is widely employed for precise state estimation in cyber-physical systems (CPSs). However, this integration exposes the system to stealthy deception attacks that render conventional detection mechanisms ineffective. We propose an exposure framework to actively reveal such stealthy attacks without modifying sensor interfaces. The framework introduces a suspect mode in which the defender injects random exposure shakes into the nominal control inputs, thus creating a discrepancy between the defender's true state estimates and the attacker's manipulated state estimates, preventing the attack from remaining stealthy. We further derive an explicit exposure condition that characterizes the minimum shake magnitude to guarantee the finite-time exposure and a compensable condition that ensures the shakes do not degrade closed-loop performance. Simulation results based on a GNSS/INS-integrated UAV system verify the effectiveness of the proposed framework.

eess.SY↗

arXiv2026

Bridging Discrete Planning and Continuous Execution for Redundant Robot

Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators due to its simplicity and reproducibility. However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter, abrupt joint transitions, and instability near singular configurations. This work proposes a bridging framework between discrete planning and continuous execution without modifying the discrete planner itself. On the planning side, step-normalized 26-neighbor Cartesian actions and a geometric tie-breaking mechanism are introduced to suppress unnecessary turns and eliminate step-size oscillations. On the execution side, a task-priority damped least-squares (TP-DLS) inverse kinematics layer is implemented. This layer treats end-effector position as a primary task, while posture and joint centering are handled as subordinate tasks projected into the null space, combined with trust-region clipping and joint velocity constraints. On a 7-DoF manipulator in random sparse, medium, and dense environments, this bridge raises planning success in dense scenes from about 0.58 to 1.00, shortens representative path length from roughly 1.53 m to 1.10 m, and while keeping end-effector error below 1 mm, reduces peak joint accelerations by over an order of magnitude, substantially improving the continuous execution quality of voxel-based RL paths on redundant manipulators.

cs.RO↗

arXiv2026

Communication-Aware Synthesis of Safety Controller for Networked Control Systems

Networked control systems (NCS) are widely used in safety-critical applications, but they are often analyzed under the assumption of ideal communication channels. This work focuses on the synthesis of safety controllers for discrete-time linear systems affected by unknown disturbances operating in imperfect communication channels. The proposed method guarantees safety by constructing ellipsoidal robust safety invariant (RSI) sets and verifying their invariance through linear matrix inequalities (LMI), which are formulated and solved as semi-definite programming (SDP). In particular, our framework simultaneously considers controller synthesis and communication errors without requiring explicit modeling of the communication channel. A case study on cruise control problem demonstrates that the proposed controller ensures safety in the presence of unexpected disturbances and multiple communication imperfections simultaneously.

eess.SY↗

arXiv2026

AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos

Long-term language-guided referring in fixed-view videos is challenging: the referent may be occluded or leave the scene for long intervals and later re-enter, while framewise referring pipelines drift as re-identification (ReID) becomes unreliable. AR2-4FV leverages background stability for long-term referring. An offline Anchor Bank is distilled from static background structures; at inference, the text query is aligned with this bank to produce an Anchor Map that serves as persistent semantic memory when the referent is absent. An anchor-based re-entry prior accelerates re-capture upon return, and a lightweight ReID-Gating mechanism maintains identity continuity using displacement cues in the anchor frame. The system predicts per-frame bounding boxes without assuming the target is visible in the first frame or explicitly modeling appearance variations. AR2-4FV achieves +10.3% Re-Capture Rate (RCR) improvement and -24.2% Re-Capture Latency (RCL) reduction over the best baseline, and ablation studies further confirm the benefits of the Anchor Map, re-entry prior, and ReID-Gating.

cs.CV↗

arXiv2026

Radio Study of Vela X Cocoon

The evolution of pulsar Wind Nebulae (PWNe) influences how high energy particles in the vicinity are generated and transport. The Vela PWN (only $\sim300$\,pc away), provides a rather rare case between young and well-evolved systems. We therefore performed new 6 and 16\,cm high-resolution observations of the Vela X Cocoon region with the Australia Telescope Compact Array (ATCA). The observations reveal a complex region with a $\sim0.5^\circ$ major curved filament extending to far south from the pulsar, as well as other intersecting filaments and wisps. Our spectral analysis hints its connection with the PWN. Our results also found strongly linearly polarized emission, ordered and tangential $B$-field to the filaments. We find the rotation measure (RM) and polarization fraction (PF) along the filament are anti-correlated with the total intensity. We develop a simple 3D model of a spiral filament to explain these, while the PF distribution requires external interpretations such as interaction with the reverse shock. Comparison with archival data suggests that large scale features like the major filament are generally stable and large motions near the X-ray filament, all these confirm the distinction between radio and X-ray features.

astro-ph.HE↗

arXiv2026

OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows

Data incompleteness severely impedes the reliability of multimodal systems. Existing reconstruction methods face distinct bottlenecks: conventional parametric/generative models are prone to hallucinations due to over-reliance on internal memory, while retrieval-augmented frameworks struggle with retrieval rigidity. Critically, these end-to-end architectures are fundamentally constrained by Semantic-Detail Entanglement -- a structural conflict between logical reasoning and signal synthesis that compromises fidelity. In this paper, we present \textbf{\underline{O}}mni-\textbf{\underline{M}}odality \textbf{\underline{G}}eneration Agent (\textbf{OMG-Agent}), a novel framework that shifts the paradigm from static mapping to a dynamic coarse-to-fine Agentic Workflow. By mimicking a \textit{deliberate-then-act} cognitive process, OMG-Agent explicitly decouples the task into three synergistic stages: (1) an MLLM-driven Semantic Planner that resolves input ambiguity via Progressive Contextual Reasoning, creating a deterministic structured semantic plan; (2) a non-parametric Evidence Retriever that grounds abstract semantics in external knowledge; and (3) a Retrieval-Injected Executor that utilizes retrieved evidence as flexible feature prompts to overcome rigidity and synthesize high-fidelity details. Extensive experiments on multiple benchmarks demonstrate that OMG-Agent consistently surpasses state-of-the-art methods, maintaining robustness under extreme missingness, e.g., a $2.6$-point gain on CMU-MOSI at $70$\% missing rates.

cs.AI↗

arXiv2026

Topologically robust programmable logic arrays using light and matter skyrmions

Photonic computing offers a low-power, high-bandwidth paradigm for information processing; however, the analogue nature of conventional architectures means that intrinsic noise and fabrication imperfections greatly impact performance, thereby severely limiting scalability. Recent work on optical skyrmions offers a route to overcoming these limitations by exploiting perturbation-resilient topological invariants assigned to the optical field for computation. Crucially, owing to its relative novelty, an architectural perspective on integrating individual components that manipulate topological charge into a functional system remains an important open goal. In this paper, we take concrete steps toward system-level design by introducing a platform-independent architecture for skyrmion-based logic, built around a modular library of topologically robust optical primitives, including generators, converters, registers, and adders. This framework enables the synthesis and arithmetic manipulation of topological numbers within a unified programmable architecture. We then experimentally validate this approach using multichannel arrays, demonstrating accurate charge readout and high robustness against alignment errors and environmental noise. These results provide a scalable foundation for topologically robust programmable logic arrays, paving the way for compact and integrated photonic processing circuits.

physics.optics↗

arXiv2025

40Gbps Tri-type Quantum Random Number Generator

Traditional quantum random number generators can produce only one type of random number, while the optimal distribution of random numbers for different applications is usually distinct. The typical solution to this challenge is either using different quantum phenomena for different types of random number, or converting one distribution of random numbers to another type. However, the former solution requires multiple hardware systems, while the latter one sacrifices a lot of secure bits. Here, we develop a quantum random number generator that can on-demand produce three distribution types of random numbers at over 60 Gbits/s (Gbps) raw bits by measuring the quantum vacuum noise. After randomness extraction, over 42 Gbps secure bit rate is demonstrated for uniform random numbers, and over 14 Gbps secure bit rate for Gaussian random number. Due to the lack of Rayleigh randomness extraction, only denoised Rayleigh raw bits are generated. Switching between different types of random numbers is achieved in electronics, which does not affect the generation rate. The random numbers pass NIST and Dieharder tests, and are available for various applications, which can be continuously accessed via Cisco Quantum Random Number web service.

quant-ph↗

arXiv2025

MigrationBench: Repository-Level Code Migration Benchmark from Java 8

With the rapid advancement of powerful large language models (LLMs) in recent years, a wide range of software engineering tasks can now be addressed using LLMs, significantly enhancing productivity and scalability. Numerous benchmark datasets have been developed to evaluate the coding capabilities of these models, while they primarily focus on code generation and issue-resolution tasks. In contrast, we introduce a new coding benchmark MigrationBench with a distinct focus: code migration. MigrationBench aims to serve as a comprehensive benchmark for migration from Java 8 to the latest long-term support (LTS) versions (Java 17, 21), including a full dataset and its subset selected with 5,102 and 300 repositories respectively. selected is a representative subset curated for complexity and difficulty, offering a versatile resource to support research in the field of code migration. Additionally, we provide a comprehensive evaluation framework to facilitate rigorous and standardized assessment of LLMs on this challenging task. We further propose an agentic framework and demonstrate that LLMs can effectively tackle repository-level code migration to Java 17. For the selected subset with Claude-4.5-Sonnet, our agentic framework achieves 71.67% and 53.33% success rate (pass@1) for minimal and maximal migration respectively. The dataset and evaluation source code are available at: https://huggingface.co/collections/AmazonScience/migrationbench and https://github.com/amazon-science/MigrationBench respectively.

cs.SE↗

arXiv2025

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency

With the increasing adoption of large language models (LLMs) in software engineering, the Chain of Thought (CoT) reasoning paradigm has become an essential approach for automated code repair. However, the explicit multi-step reasoning in CoT leads to substantial increases in token consumption, reducing inference efficiency and raising computational costs, especially for complex code repair tasks. Most prior research has focused on improving the correctness of code repair while largely overlooking the resource efficiency of the reasoning process itself. To address this challenge, this paper proposes three targeted optimization strategies: Context Awareness, Responsibility Tuning, and Cost Sensitive. Context Awareness guides the model to focus on key contextual information, Responsibility Tuning refines the structure of the reasoning process through clearer role and responsibility assignment, and Cost Sensitive incorporates resource-awareness to suppress unnecessary token generation during inference. Experiments across diverse code repair scenarios demonstrate that these methods can significantly reduce token consumption in CoT-based reasoning without compromising repair quality. This work provides novel insights and methodological guidance for enhancing the efficiency of LLM-driven code repair tasks in software engineering.

cs.SE↗

arXiv2025

Radio Observation of the Pulsar Wind Nebula in SNR G11.2-0.3

Pulsar wind nebulae (PWNe) are important sources for understanding galactic high-energy processes, but it is controversial until now about how high-energy particles in PWNe are accelerated and transported. Lacking radio counterparts of X-ray PWNe (the proposed acceleration sites) introduce difficulties to better understandings in multi wavelengths. Our recent 3, 6, and 16\,cm high-resolution observations of G11.2$-$0.3 PWN with the Australia Telescope Compact Array (ATCA) uniquely show morphological similarity with its X-ray PWN (a torus/jet feature). Spectral indices of the radio torus and jet are around -0.09 and -0.10, respectively. Meanwhile for the jet region, the spectral break between radio and X-ray spectra implies particle acceleration mechanisms other than a diffusive shock acceleration. Polarization results suggest a helical B-field inside the jet, the equipartition B-field strength of which is below 100\,$\mu$G.

astro-ph.HE↗

arXiv2024

GenPod: Constructive News Framing in AI-Generated Podcasts More Effectively Reduces Negative Emotions Than Non-Constructive Framing

AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing styles-constructive versus non-constructive-affect audience responses in AI-generated podcasts. We developed a pipeline using generative AI and text-to-speech (TTS) technology to create both constructive and non-constructive news podcasts from the same set of news resources. Through empirical research (N=65), we found that constructive podcasts significantly reduced audience's negative emotions compared to non-constructive podcasts. Additionally, in certain news contexts, constructive framing might further enhance audience self-efficacy. Our findings show that simply altering the framing of AI generated content can significantly impact audience responses, and we offer insights on leveraging these effects for positive outcomes while minimizing ethical risks.

cs.HC↗