arXiv subjects

Zepeng Liu

Publications and source records attributed to Zepeng Liu.

6 recordsLinked to original sources

arXiv2026

GRBench: A Comprehensive Benchmark Evaluation for Graph-relational Data Management

Modern data-intensive applications increasingly require database systems to manage structured records and graph data. This demand gives rise to graph-relational data management, spanning storage, query processing, and optimization across relational and graph data. In response, relational database extensions, multi-model databases, and dedicated graph-relational systems have emerged with diverse architectures. However, evaluation methodologies have not kept pace. Existing relational and graph benchmarks assess the two models largely in isolation, while multi-model benchmarks provide limited coverage of graph-relational workloads. Available graph-relational workloads mainly support functional validation and end-to-end latency measurement, revealing little about how storage, operator, and optimization designs affect performance. To evaluate system capabilities in graph-relational data management, we present GRBench. First, GRBench constructs a linked graph-relational schema from the real-world SciSciNet-v2 dataset and derives scalable instances through consistency-preserving subset extraction. Second, it organizes purpose-built query series for controlled evaluation of query processing and system components. Third, GRBench provides semantically equivalent native query formulations and evaluates representative system architectures through a unified, multidimensional methodology. Based on this evaluation, we analyze design trade-offs and identify open challenges to guide future system design and optimization.

cs.DB↗

arXiv2026

LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring

We present LoRM (Language of Rotating Machinery), a self-supervised framework for multi-modal rotating-machinery signal understanding and real-time condition monitoring. LoRM is built on the idea that rotating-machinery signals can be viewed as a machine language: local signals can be tokenised into discrete symbolic units, and their future evolution can be predicted from observed multi-sensor context. Unlike conventional signal-processing methods that rely on hand-crafted transforms and features, LoRM reformulates multi-modal sensor data as a token-based sequence-prediction problem. For each data window, the observed context segment is retained in continuous form, while the future target segment of each sensing channel is quantised into a discrete token. Then, efficient knowledge transfer is achieved by partially fine-tuning a general-purpose pre-trained language model on industrial signals, avoiding the need to train a large model from scratch. Finally, condition monitoring is performed by tracking token-prediction errors as a health indicator, where increasing errors indicate degradation. In-situ tool condition monitoring (TCM) experiments demonstrate stable real-time tracking and strong cross-tool generalisation, showing that LoRM provides a practical bridge between language modelling and industrial signal analysis. The source code is publicly available at https://github.com/Q159753258/LormPHM.

cs.CL↗

arXiv2026

Graph-centric Cross-model Data Integration and Analytics in a Unified Multi-model Database

Graph-centric cross-model data integration and analytics (GCDIA) refer to tasks that leverage the graph model as a central paradigm to integrate relevant information across heterogeneous data models, such as relational and document, and subsequently perform complex analytics such as regression and similarity computation. As modern applications generate increasingly diverse data and move beyond simple retrieval toward advanced analytical objectives (e.g., prediction and recommendation), GCDIA has become increasingly important. Existing multi-model databases (MMDBs) struggle to efficiently support both integration (GCDI) and analytics (GCDA) in GCDIA. They typically separate graph processing from other models without global optimization for GCDI, while relying on tuple-at-a-time execution for GCDA, leading to limited performance and scalability. To address these limitations, we propose GredoDB, a unified MMDB that natively supports storing graph, relational, and document models, while efficiently processing GCDIA. Specifically, we design 1) topology- and attribute-aware graph operators for efficient predicate-aware traversal, 2) a unified GCDI optimization framework to exploit cross-model correlations, and 3) a parallel GCDA architecture that materializes intermediate results for operator-level execution. Experiments on the widely adopted multi-model benchmark M2Bench demonstrate that, in terms of response time, GredoDB achieves up to 107.89 times and an average of 10.89 times speedup on GCDI, and up to 356.72 times and an average of 37.79 times on GCDA, compared to state-of-the-art (SOTA) MMDBs.

cs.DB↗

arXiv2025

Temporal SU(1,1) Interferometer with Broadband Squeezed Light Injection

Temporal optics has attracted much attention due to its ability for lossless stretching of ultrafast temporal pulses. At the same time, spatial SU(1,1) interferometers have been widely used because of their high sensitivity to phase changes. On this basis, we studied a temporal SU(1,1) interferometer based on a temporal Fourier transform system and injected broadband squeezing light into the interferometer for research. The results show that the output spectral characteristics of the interferometer depend on the ratio of the focal group velocity dispersion (GDD) of the two temporal lenses (this ratio is defined as the scaling factor $M$) and the phase derivative of the applied phase. The scaling factor $M$ significantly affects the bandwidth and squeezing degree of the output spectrum. The phase derivative induces a frequency-shift effect, and the magnitude of the shift exhibits a linear relationship to the phase derivative. Furthermore, in the output squeezed-state spectrum, the distribution of squeezing degree concentrates at the center frequency and at positions where frequency shifts occur. As the value of scaling factor $M$ increases, the proportion of squeezing degree allocated at the center frequency correspondingly increases. This temporal SU(1,1) interferometer architecture opens new avenues for the control of non-classical fields in the time-frequency domain and quantum information processing applications.

physics.optics↗

arXiv2024

On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means

The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large-scale datasets, existing k-means algorithms have been developed to achieve high performance with significant computational resources, such as memory and CPU usage time. These algorithms, though effective, are not well-suited for resource-constrained devices. In this paper, we propose a fast, memory-efficient, and cost-predictable k-means called Dask-means. We first accelerate k-means by designing a memory-efficient accelerator, which utilizes an optimized nearest neighbor search over a memory-tunable index to assign spatial vectors to clusters in batches. We then design a lightweight cost estimator to predict the memory cost and runtime of the k-means task, allowing it to request appropriate memory from devices or adjust the accelerator's required space to meet memory constraints, and ensure sufficient CPU time for running k-means. Experiments show that when simplifying datasets with scale such as $10^6$, Dask-means uses less than $30$MB of memory, achieves over $168$ times speedup compared to the widely-used Lloyd's algorithm. We also validate Dask-means on mobile devices, where it demonstrates significant speedup and low memory cost compared to other state-of-the-art (SOTA) k-means algorithms. Our cost estimator estimates the memory cost with a difference of less than $3\%$ from the actual ones and predicts runtime with an MSE up to $33.3\%$ lower than SOTA methods.

cs.LG↗

arXiv2023

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge

Image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception.

cs.CV↗