arXiv subjects

Wang Gao

Publications and source records attributed to Wang Gao.

At least 19 recordsLinked to original sources

arXiv2026

Dual-Satellite Doppler Accuracy Prediction and Geometry Selection for Sparse LEO Signals of Opportunity

Low Earth Orbit (LEO) satellites have emerged as a promising complement to GNSS for positioning in signal challenged environments. In sparse LEO signals of opportunity scenarios, Doppler positioning often relies on only one or two satellite passes, making positioning accuracy highly dependent on pass geometry. This paper investigates dual satellite LEO Doppler accuracy prediction and geometry selection. A single pass Doppler accuracy model based on the Doppler Dilution of Precision (DDOP) framework is first validated using real Iridium measurements. An information domain fusion model is then developed to combine the effective position information from two satellite passes while accounting for pass specific clock parameters. Based on this model, an analytical relationship between the intersection angle of the two predicted error ellipses and the fused positioning accuracy is derived and verified through simulations. Long term ORBCOMM observations are further used to evaluate the practical availability of favorable satellite pairs. Results show that an intersection angle of about 20{\deg} is sufficient to achieve approximately 50 m theoretical positioning accuracy, and that such complementary satellite pairs are typically available within about 30 min. These results provide practical guidance for geometry aware satellite selection and observation scheduling in sparse LEO Doppler positioning.

eess.SP↗

arXiv2026

ChartSync: A Benchmark for Visuo-Logical Cascading Chart Editing

Generative image editing models struggle with structured statistical charts when data modifications require geometric synchronization. We formalize this task as Visuo-Logical Cascading Editing (VLCE). However, existing methods remain confined to localized text substitutions and struggle with dependency-aware cascading updates. To systematically evaluate this capability, we introduce ChartSync, an expert-validated benchmark constructed via a programmatic rendering pipeline that guarantees deterministic visuo-logical coupling for the ground truth. ChartSync comprises 870 triplets across 9 chart categories and 4 task types, including 235 geometry-coupled VLCE instances that specifically test cascading text-to-geometry synchronization. We further evaluate these instances via a two-tier framework combining objective visual metrics with a vision-language model judge paradigm to assess low-level fidelity alongside multimodal comprehension and reasoning. Evaluating 14 image editing models and one code-mediated pipeline reveals a nuanced capability gap: most open-source models suffer severe drops in geometric synchronization, while only two frontier proprietary models show emerging VLCE capability, with their residual errors mainly involving semantic isolation and background corruption. Our detailed error analysis deconstructs these failure paradigms to identify core meta-abilities for guiding future multimodal architectures. The ChartSync dataset and code are publicly released at https://github.com/kaka-yjk/ChartSyncCodebase.

cs.CV↗

arXiv2026

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice

While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legally regulated Chinese tax domain. Consequently, while tax-related benchmarks are gaining attention, many focus on isolated NLP tasks, neglecting real-world practical capabilities. To address this issue, we introduce TaxPraBen, the first dedicated benchmark for Chinese taxation practice. It combines 10 traditional application tasks, along with 3 pioneering real-world scenarios: tax risk prevention, tax inspection analysis, and tax strategy planning, sourced from 14 datasets totaling 7.3K instances. TaxPraBen features a scalable structured evaluation paradigm designed through process of "structured parsing-field alignment extraction-numerical and textual matching", enabling end-to-end tax practice assessment while being extensible to other domains. We evaluate 19 LLMs based on Bloom's taxonomy. The results indicate significant performance disparities: all closed-source large-parameter LLMs excel, and Chinese LLMs like Qwen2.5 generally exceed multilingual LLMs, while the YaYi2 LLM, fine-tuned with some tax data, shows only limited improvement. TaxPraBen serves as a vital resource for advancing evaluations of LLMs in practical applications.

cs.CL↗

arXiv2026

Determinants of Self-Interstitial Energetics in Refractory High-Entropy Alloys

Self-interstitials play a central role in governing the mechanical and anti-irradiation properties of refractory high-entropy alloys (RHEAs), however, the prediction of interstitial formation energies (Ef) is formidable due to the chemically complex environments in RHEAs. Herein, we develop a framework based on the tight-binding model to quantify the effects of complex alloying and lattice distortion on Ef. Our scheme reveals that Ef is jointly determined by the average d-band center of RHEAs and the d-band width of interstitial sites. Notably, the d-band width mainly depends on the interatomic hopping matrix and atomic size-determined coordination number, which together make the metallic bonding around interstitials in RHEAs resemble the distance-dependence law of van der Waals forces. By capturing d-band coupling character, our descriptor describes both interstitial configurations within a universal framework. Our model reveals a new physical picture of interstitial formation, providing a useful tool for the design of high-performance RHEAs.

cond-mat.mtrl-sci↗

arXiv2025

A Preliminary Exploration of the Differences and Conjunction of Traditional PNT and Brain-inspired PNT

Developing universal Positioning, Navigation, and Timing (PNT) is our enduring goal. Today's complex environments demand PNT that is more resilient, energy-efficient and cognitively capable. This paper asks how we can endow unmanned systems with brain-inspired spatial cognition navigation while exploiting the high precision of machine PNT to advance universal PNT. We provide a new perspective and roadmap for shifting PNT from "tool-oriented" to "cognition-driven". Contributions: (1) multi-level dissection of differences among traditional PNT, biological brain PNT and brain-inspired PNT; (2) a four-layer (observation-capability-decision-hardware) fusion framework that unites numerical precision and brain-inspired intelligence; (3) forward-looking recommendations for future development of brain-inspired PNT.

cs.RO↗

arXiv2025

An Analytic Model to Determine the Interstitial-Solute Energetics and Underlying Mechanism in Refractory High-Entropy Alloys

The solution and diffusion of interstitial non-metallic solutes (INSs) like H, He, O, C, N, P, and S is common in refractory high-entropy alloys (RHEAs) and essentially controls the RHEAs properties. However, the disorder local chemical environments of RHEAs hinder the quantitative prediction of the stability and diffusivity of INSs and the understanding of the underlying mechanism. Based on the tight-binding models, we propose an analytic model for determining the stability and diffusivity of INSs in RHEAs, by approximating the bonding length between INSs and their neighbors with the atomic radius of the neighbors in elemental states. This predictive model identifies that the energetics of INSs depends linearly on the d-band width of their neighbors, with the slope determined by the valence of INSs. Our scheme provides an electronic-level understanding of INSs in RHEAs and explains key experimental observations, which can serve as an effective tool for designing advanced RHEAs.

cond-mat.mtrl-sci↗

arXiv2025

Draw2Cut: Direct On-Material Annotations for CNC Milling

Creating custom artifacts with computer numerical control (CNC) milling machines typically requires mastery of complex computer-aided design (CAD) software. To eliminate this user barrier, we introduced Draw2Cut, a novel system that allows users to design and fabricate artifacts by sketching directly on physical materials. Draw2Cut employs a custom-drawing language to convert user-drawn lines, symbols, and colors into toolpaths, thereby enabling users to express their creative intent intuitively. The key features include real-time alignment between material and virtual toolpaths, a preview interface for validation, and an open-source platform for customization. Through technical evaluations and user studies, we demonstrate that Draw2Cut lowers the entry barrier for personal fabrication, enabling novices to create customized artifacts with precision and ease. Our findings highlight the potential of the system to enhance creativity, engagement, and accessibility in CNC-based woodworking.

cs.HC↗

arXiv2024

Analytic model for grain-boundary segregation ener-gies in metal polycrystal

Solute segregation at grain boundaries (GBs) of polycrystals strongly impacts the mechanical properties of metals including strength, fracture, embrittlement, and corrosion. However, the complexity of GB structures and the large chemical space of solutes and matrices impede the understanding of segregation. Herein, we identify a physical-based determinant, by unifying the effects of plastic strain and bonding breaking, for determining the segregation energies at GBs. By further combining with the usual coordination number, atomic radius of solutes and matrices, and cohesive energy of matrices, we build an analytic framework to predict segregation energies of polycrystal GBs across various solutes and matrices. These findings indicate an unusual Coulombic-like and localized nature of the bonding at polycrystal GBs and bulk metallic glasses (BMGs). Our scheme not only uncovers the coupling rule of solutes and matrices for GB segregation in polycrystals, but also provides an effective tool for the design of high-performance alloys.

cond-mat.mtrl-sci↗

arXiv2024

Correlating Local Lattice Distortion with Dislocation Pinning in Refractory High-Entropy Alloys

Local lattice distortion (LLD) of refractory high-entropy alloys (RHEAs) plays an essential role in mechanical properties and phase stability. However, the random distribution of multi-principal constituents of RHEAs inhibits the comprehension of LLD, although LLD is suggested to couple with chemical short-range-order (SRO). Herein, an analytical model is built to determine the site-to-site LLD of RHEAs by coupling the local lattice sites, the local size ordering in their environments and the global constituent information. By elucidating the size coupling between components, the model demonstrates that LLD exhibits a mechanism similar to the relaxation of metal surfaces. Moreover, it is found that LLD, rather than chemical SRO, serves as the origin of solid-solution strengthening and as a measure of the phase transformation in RHEAs. The scheme provides a comprehensive physical picture and offers a quantitative measurement of LLD at macro and micro scales, laying a foundation for the design of RHEAs.

cond-mat.mtrl-sci↗

arXiv2024

Understanding the intrinsic framework of the Hall-Petch relationship of metals from the view of the electronic-structure level

The relationship between grain size and yield strength of metals follows the Hall-Petch relationship σ = σ0 + kd^-0.5; however, the specific physical factors that affect the coefficients σ0 and k of this relationship remain unclear. Here we propose the intrinsic descriptors to determine the Hall-Petch relation across different metals and alloys. Inspired by the tight-binding theory, we find that σ0 strongly depends on the group and period number, the valence-electron number and electronegativity, while k is determined by the cohesive energy. Our framework establishes a predictive structure-property relationship for the size-dependent yield strength of various metals, and unravels that both the coefficients of the Hall-Petch relationship physically originate from the d-band properties. This novel correlation provides a new perspective for understanding the mechanical strength of metals, which is useful for the design of high-performance materials.

cond-mat.mtrl-sci↗

arXiv2023

HODOR: Shrinking Attack Surface on Node.js via System Call Limitation

Node.js provides Node.js applications with system interaction capabilities using system calls. However, such convenience comes with a price, i.e., the attack surface of JavaScript arbitrary code execution (ACE) vulnerabilities is expanded to the system call level. There lies a noticeable gap between existing protection techniques in the JavaScript code level (either by code debloating or read-write-execute permission restriction) and a targeted defense for emerging critical system call level exploitation. To fill the gap, we design and implement HODOR, a lightweight runtime protection system based on enforcing precise system call restrictions when running a Node.js application. HODOR achieved this by addressing several nontrivialial technical challenges. First, HODOR requires to construct high-quality call graphs for both the Node.js application (in JavaScript) and its underlying Node.js framework (in JavaScript and C/C++). Specifically, HODOR incorporates several important optimizations in both the JavaScript and C/C++ level to improve the state-of-the-art tools for building more precise call graphs. Then, HODOR creates the main-thread whitelist and the thread-pool whitelist respectively containing the identified necessary system calls based on the call graphs mappings. Finally, with the whitelists, HODOR implements lightweight system call restriction using the Linux kernel feature Secure Computing Mode (seccomp) to shrink the attack surface. We utilize HODOR to protect 83 real-world Node.js applications compromised by arbitrary code/command execution attacks. HODOR could reduce the attack surface to 16.75% on average with negligible runtime overhead (i.e., <3%).

cs.CR↗

arXiv2023

A Local Concentration-based Descriptor Predicting the Stacking Fault Energy of Refractory High Entropy Alloys

Stacking fault energy (SFE) is an essential parameter for characterizing mechanical properties. However, in high entropy alloys (HEAs), the local chemical environment varies significantly across different stacking fault planes, resulting in a substantial fluctuation of SFE values rather than a unique value, which prohibits the prediction of the local SFE. Herein, we proposed an effective descriptor based on the local concentration ratio near stacking fault to quantitatively predict the local SFE of refractory HEAs. We find that the role of a given element in determining SFE strongly depends on its valence-electron number relative to other components and the contribution of its s- and d-electrons to its cohesive properties, which can be understood in the framework of the tight-binding model. Notably, the descriptor not only unifies the local nature of SFE from simple alloys to HEAs but also helps to quickly design HEAs as the involved parameters are easily accessible.

cond-mat.mtrl-sci↗

arXiv2023

Cooperative trajectory planning algorithm of USV-UAV with hull dynamic constraints

Efficient trajectory generation in complex dynamic environments remains an open problem in the unmanned surface vehicle (USV). The perception of the USV is usually interfered with by the swing of the hull and the ambient weather, making it challenging to plan the optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed to ensure that USV can execute a safe and smooth path in the process of autonomous advance in multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. And then, an initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

cs.RO↗

arXiv2022

A Rule of Solute Segregation at Grain Boundaries

The control of solute segregation at grain boundaries (GBs) is essential in engineering alloy properties, however the structure-activity relationship of the key parameter-the segregation energies-still remains elusive. Here we propose the electronic and geometric descriptors of GB segregation based on the valence, electronegativity and size of solutes and the non-local coordination number of free surfaces, with which we build a predictive framework to determine the segregation energies across different solutes, matrices, GB structures and segregation sites. This framework uncovers not only the coupling rule of solutes and matrices in GB segregation, but also the origin of solute-segregation determinants. The contribution of solutes essentially stems from their d- and s-state coupling in alloying, whereas that of matrix GB interfaces is determined by matrix free surfaces. Our scheme builds a novel picture for the solute segregation at GBs and provides a useful tool for the design of advanced alloys.

cond-mat.mtrl-sci↗

arXiv2022

Determinants of local chemical environments and magnetic moments of high-entropy alloys

High-entropy alloys (HEAs) such as CrMnFeCoNi exhibit unconventional mechanical properties due to their compositional disorder. However, it remains a formidable challenge to estimate the local chemical-environment and magnetic effects of HEAs. Herein we identify the state-associated cohesive energy and band filling originated from the tight-binding and Friedel models as descriptors to quantify the site-to-site chemical bonding and magnetic moments of HEAs. We find that the s-state cohesive energy is indispensable in determining the bonding-strength trend of CrMnFeCoNi that differs from the bonding characteristics of precious and refractory HEAs, while the s-band filling is effective in determining the magnetic moments. This unusual behavior stems from the unique chemical and magnetic nature of Cr atoms and is essentially due to the localized and transferred itinerant electrons. Our study establishes a fundamental physical picture of chemical bonding and magnetic interactions of HEAs and provides a rational guidance for designing advanced structural alloys.

cond-mat.mtrl-sci↗

arXiv2021

Predictive model of surface adsorption in dissolution on transition metals and alloys

Surface adsorption, which is often coupled with surface dissolution, is generally unpredictable on alloys due to the complicated alloying and dissolution effects. Herein, we introduce the electronic gradient and cohesive properties of surface sites to characterize the effects of alloying and dissolution. This enables us to build a predictive model for the quantitative determination of the adsorption energy in dissolution, which holds well for transition metals, near-surface alloys, binary alloys, and high-entropy alloys. Furthermore, this model uncovers a synergistic mechanism between the d-band upper-edge ratio, d-band width and s-band depth in determining the alloying and dissolution effects on adsorption. Our study not only provides fundamental mechanistic insights into surface adsorption on alloys but also offers a long-sought tool for the design of advanced alloy catalysts.

cond-mat.mtrl-sci↗

arXiv2021

A Transferable Machine-learning Scheme from Pure Metals to Alloys in Predicting Adsorption Energies

Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms' valence, electronegativity and coordination and the adsorbates' valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts.

cond-mat.mtrl-sci↗

arXiv2021

Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing \sArt competitors by 6.3\% (58.6\% vs. 52.3\%) in terms of the AP metric.The code is available at https://github.com/yuhuan-wu/RDPNet.

cs.CV↗