arXiv subjects

Wang Rui

Publications and source records attributed to Wang Rui.

8 recordsLinked to original sources

arXiv2026

DoDTrack: Indoor Mobile Devices Tracking via Difference-of-Doppler

In this paper, the Doppler frequency shift (DFS) is exploited as the only sensing parameter for low-cost indoor mobile device tracking. The existing trajectory tracking methods via DFS of Wi-Fi systems often require the knowledge of the starting position or additional information, like angle-of-arrival (AoA) and time-of-flight (ToF), to recover the trajectory of a moving target. This paper proposes the DoDTrack, a novel Difference-of-Doppler (DoD)-based tracking system, to track an active mobile device using a single receiver with distributed antennas. By comparing the signals received at the distributed receive antennas, which share the oscillator, the DoDs among the antennas can be detected robustly. Then, the reconstruction of the trajectory without prior knowledge of the trajectory starting position can be formulated as a minimum mean square error (MMSE) problem, which can be solved via alternating optimization. Particularly, the starting position and the trajectory shape are updated alternately in the proposed algorithm. In performance validation, we implemented the proposed DoDTrack design on a USRP-X310 platform, and assessed its estimation accuracy with various trajectory shapes in an indoor environment. Experimental results demonstrate that DoDTrack achieves a median tracking error of 0.34 m within a 6 m $\times$ 6 m sensing area, offering a high-precision and low-cost solution for active device tracking.

eess.SP↗

arXiv2026

Comprehensive pKa Data Augmentation from Limited Real Data through an Engineered Models-Quantum Framework

Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling. Building on iBonD, the largest experimental pKa database established, we and other researchers have developed several methods including machine-learning-based empirical prediction and high-accuracy energy calculations. Despite this foundation, the rapid augmentation of high-quality pKa data remains fundamentally constrained. As part of this work, we performed large-scale regression-based pKa prediction on unlabeled molecular datasets using a collection of extensively optimized machine-learning models. The results indicate that, since the feature distributions of unlabeled molecular datasets, the pKa data distribution approximates normality, with extreme scarcity of tail-region samples. Although such augmentation is highly valuable for improving overall data availability and predictive modeling, it remains insufficient for efficiently discovering molecules with broad-spectrum pKa properties. To address this, we explore the targeted generation of molecules with sparse pKa properties from the vast chemical space. Given that traditional continuous latent space VAE-RNN methods for molecular generation suffer from insufficient stability and fail to demonstrate clear advantages in complementing sparse data, we design and implement a quantum-assisted sparse-pKa molecular generation. Feasibility is validated on a simulated quantum annealer, and superior extreme-value sampling is further achieved on physical coherent Ising machines (CIMs). (to be continued)

physics.chem-ph↗

arXiv2026

Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model

Quantum computing devices are recognized as powerful tools for solving NP-complete problems. However, the intricacy of their modeling presents notable barriers for non-specialists, while the tedious iteration of constraint weights and modeling methodologies also consumes substantial effort on the part of experts. To address these challenges, this study integrates a femtosecond laser-pumped Coherent Ising Machine (CIM) with an LLM-driven agentic system by leveraging the LangGraph and LangChain frameworks. Comprehensive investigations demonstrate that large language models (LLMs) can effectively perform such tasks in modeling as QUBO/Ising model calibration, constraint weight decision iteration and rapid validation of literature-reported schemes. Notably, all these tasks can be fully implemented based on domestic large models, combined with domestically developed CIM hardware, we truly achieve the practical empowerment of quantum CIM that fully relies on all-domestic agentic large models and hardware. This work successfully realizes robust technological integration, laying a solid foundation for subsequent research. Nevertheless, it also identifies the persisting challenges in the two cutting-edge fields of large models and quantum computing at the current stage. Encouragingly, we unexpectedly discover a promising new paradigm where accumulated knowledge from agent-assisted quantum computing iterations reciprocally enhances the agent's own problem-solving capability, thereby addressing these challenges.

cs.AI↗

arXiv2025

MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference

The increasing demand for deep neural inference within database environments has driven the emergence of AI-native DBMSs. However, existing solutions either rely on model-centric designs requiring developers to manually select, configure, and maintain models, resulting in high development overhead, or adopt task-centric AutoML approaches with high computational costs and poor DBMS integration. We present MorphingDB, a task-centric AI-native DBMS that automates model storage, selection, and inference within PostgreSQL. To enable flexible, I/O-efficient storage of deep learning models, we first introduce specialized schemas and multi-dimensional tensor data types to support BLOB-based all-in-one and decoupled model storage. Then we design a transfer learning framework for model selection in two phases, which builds a transferability subspace via offline embedding of historical tasks and employs online projection through feature-aware mapping for real-time tasks. To further optimize inference throughput, we propose pre-embedding with vectoring sharing to eliminate redundant computations and DAG-based batch pipelines with cost-aware scheduling to minimize the inference time. Implemented as a PostgreSQL extension with LibTorch, MorphingDB outperforms AI-native DBMSs (EvaDB, Madlib, GaussML) and AutoML platforms (AutoGluon, AutoKeras, AutoSklearn) across nine public datasets, encompassing series, NLP, and image tasks. Our evaluation demonstrates a robust balance among accuracy, resource consumption, and time cost in model selection and significant gains in throughput and resource efficiency.

cs.DB↗

arXiv2023

A privacy-preserving publicly verifiable quantum random number generator

Verifying the quality of a random number generator involves performing computationally intensive statistical tests on large data sets commonly in the range of gigabytes. Limitations on computing power can restrict an end-user's ability to perform such verification. There are also applications where the user needs to publicly demonstrate that the random bits they are using pass the statistical tests without the bits being revealed. We report the implementation of an entanglement-based protocol that allows a third party to publicly perform statistical tests without compromising the privacy of the random bits.

quant-ph↗

arXiv2018

Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks

In this study, the fundamental stellar atmospheric parameters (Teff, log g, [Fe/H] and [α/Fe]) were derived for low-resolution spectroscopy from LAMOST DR5 with Generative Spectrum Networks (GSN). This follows the same scheme as a normal artificial neural network with stellar parameters as the input and spectra as the output. The GSN model was effective in producing synthetic spectra after training on the PHOENIX theoretical spectra. In combination with Bayes framework, the application for analysis of LAMOST observed spectra exhibited improved efficiency on the distributed computing platform, Spark. In addition, the results were examined and validated by a comparison with reference parameters from high-resolution surveys and asteroseismic results. Our results show good consistency with the results from other survey and catalogs. Our proposed method is reliable with a precision of 80 K for Teff, 0.14 dex for log g, 0.07 dex for [Fe/H] and 0.168 dex for [α/Fe], for spectra with a signal-to-noise in g bands (SNRg) higher than 50. The parameters estimated as a part of this work are available at http://paperdata.china-vo.org/GSN_parameters/GSN_parameters.csv.

astro-ph.IM↗

arXiv2013

The Stability and Charge Carriers in Bilayer Silicene

The structure optimization, phonon, and ab initio finite temperature molecular dynamics calculations have been performed to predict that bilayer silicene has stable structure with AB stacking geometry and is more favorable energetically to synthesize than monolayer silicene, a two-dimensional honeycomb lattice with buckled geometry. Marvellously, its electronic bands show that the charge carriers behave like relativistic Dirac fermions with linear energy dispersions near the K points. An insightful analysis has been presented to understand the low-energy electronic excitations based on tight-binding approximation, and we suggest that the component of sp3 hybridization in the buckled geometry blocks the interlayer hopping, so the linear dispersion can be preserved.

cond-mat.mes-hall↗

arXiv2011

First-principles calculations on temperature- dependent elastic constants of rare-earth intermetallic compounds:YAg and YCu

we present the temperature-dependent elastic constants of two ductile rare-earth intermetallic compounds YAg and YCu with CsCl-type B2 structure by using a first-principles approach. The elastic moduli as a function of temperature are predicted from the combination of static volumedependent elastic constants obtained by the first-principles total-energy method with density functional theory and the thermal expansion obtained by the first-principles phonon calculations with density-functional perturbation theory. The comparison between our calculated results and the available experimental data for Ag and Cu provides good agreements. In the calculated temperature $0-1000K$, the elastic constants of YAg and YCu follow a normal behavior with temperature that those decrease with increasing temperature, and satisfy the stability conditions for B2 structures. The Cauchy pressure for YAg and YCu as a function of temperature is also discussed, and our results mean that YAg and YCu become more ductile while increasing temperature.

cond-mat.mtrl-sci↗