arXiv subjects

Qiuping Wang

Publications and source records attributed to Qiuping Wang.

14 recordsLinked to original sources

arXiv2026

The Design and Implementation of a High-Performance Log-Structured RAID System for ZNS SSDs

Zoned Namespace (ZNS) defines a new abstraction for host software to flexibly manage storage in flash-based SSDs as append-only zones. It also provides a Zone Append primitive to further boost the write performance of ZNS SSDs by exploiting intra-zone parallelism. However, making Zone Append effective for reliable and scalable storage, in the form of a RAID array of multiple ZNS SSDs, is non-trivial, since Zone Append offloads address management to ZNS SSDs and requires hosts to specifically manage RAID stripes across multiple drives. We propose ZapRAID, a high-performance log-structured RAID system for ZNS SSDs by carefully exploiting Zone Append to achieve high write parallelism and lightweight stripe management. ZapRAID adopts a group-based data layout with a coarse-grained ordering across multiple groups of stripes, such that it can use small-size metadata for stripe management on a per-group basis under Zone Append. It further adopts hybrid data management to simultaneously achieve intra-zone and inter-zone parallelism through a careful combination of both Zone Write and Zone Append primitives. We implement ZapRAID as a user-space block device, and evaluate ZapRAID using microbenchmarks, trace-driven experiments, and real-application experiments. Our evaluation results show that ZapRAID achieves high write throughput and maintains high performance in normal reads, degraded reads, crash recovery, and full-drive recovery.

cs.DC↗

arXiv2025

Testing degree heterogeneity in directed networks

In this study, we focus on the likelihood ratio tests in the $p_0$ model for testing degree heterogeneity in directed networks, which is an exponential family distribution on directed graphs with the bi-degree sequence as the naturally sufficient statistic. For testing the homogeneous null hypotheses $H_0: α_1 = \cdots = α_r$, we establish Wilks-type results in both increasing-dimensional and fixed-dimensional settings. For increasing dimensions, the normalized log-likelihood ratio statistic $[2\{\ell(\widehat{\mathbfθ})-\ell(\widehat{\mathbfθ}^0)\}-r]/(2r)^{1/2}$ converges in distribution to a standard normal distribution. For fixed dimensions, $2\{\ell(\widehat{\mathbfθ})-\ell(\widehat{\mathbfθ}^0)\}$ converges in distribution to a chi-square distribution with $r-1$ degrees of freedom as $n\rightarrow \infty$, independent of the nuisance parameters. Additionally, we present a Wilks-type theorem for the specified null $H_0: α_i=α_i^0$, $i=1,\ldots, r$ in high-dimensional settings, where the normalized log-likelihood ratio statistic also converges in distribution to a standard normal distribution. These results extend the work of \cite{yan2025likelihood} to directed graphs in a highly non-trivial way, where we need to analyze much more expansion terms in the fourth-order asymptotic expansions of the likelihood function and develop new approximate inverse matrices under the null restricted parameter spaces for approximating the inverse of the Fisher information matrices in the $p_0$ model. Simulation studies and real data analyses are presented to verify our theoretical results.

math.ST↗

arXiv2025

Moment estimation in paired comparison models with a growing number of subjects

When the number of subjects, $n$, is large, paired comparisons are often sparse. Here, we study statistical inference in a class of paired comparison models parameterized by a set of merit parameters, under an Erdös--Rényi comparison graph, where the sparsity is measured by a probability $p_n$ tending to zero. We use the moment estimation base on the scores of subjects to infer the merit parameters. We establish a unified theoretical framework in which the uniform consistency and asymptotic normality of the moment estimator hold as the number of subjects goes to infinity. A key idea for the proof of the consistency is that we obtain the convergence rate of the Newton iterative sequence for solving the estimator. We use the Thurstone model to illustrate the unified theoretical results. Further extensions to a fixed sparse comparison graph are also provided. Numerical studies and a real data analysis illustrate our theoretical findings.

math.ST↗

arXiv2025

L-2 Regularized maximum likelihood for $β$-model in large and sparse networks

The $β$-model is a powerful tool for modeling large and sparse networks driven by degree heterogeneity, where many network models become infeasible due to computational challenge and network sparsity. However, existing estimation algorithms for $β$-model do not scale up. Also, theoretical understandings remain limited to dense networks. This paper brings several significant improvements over existing results to address the urgent needs of practice. We propose a new $\ell_2$-penalized MLE algorithm that can comfortably handle sparse networks of millions of nodes with much-improved memory parsimony. We establish the first rate-optimal error bounds and high-dimensional asymptotic normality results for $β$-models, under much weaker network sparsity assumptions than best existing results. Application of our method to large COVID-19 network data sets discovered meaningful results.

stat.ME↗

arXiv2025

Maximum likelihood estimation in the sparse Rasch model

The Rasch model has been widely used to analyse item response data in psychometrics and educational assessments. When the number of individuals and items are large, it may be impractical to provide all possible responses. It is desirable to study sparse item response experiments. Here, we propose to use the Erdős\textendash Rényi random sampling design, where an individual responds to an item with low probability $p$. We prove the uniform consistency of the maximum likelihood estimator %by developing a leave-one-out method for the Rasch model when both the number of individuals, $r$, and the number of items, $t$, approach infinity. Sampling probability $p$ can be as small as $\max\{\log r/r, \log t/t\}$ up to a constant factor, which is a fundamental requirement to guarantee the connection of the sampling graph by the theory of the Erdős\textendash Rényi graph. The key technique behind this significant advancement is a powerful leave-one-out method for the Rasch model. We further establish the asymptotical normality of the MLE by using a simple matrix to approximate the inverse of the Fisher information matrix. The theoretical results are corroborated by simulation studies and an analysis of a large item-response dataset.

math.ST↗

arXiv2022

An In-Depth Comparative Analysis of Cloud Block Storage Workloads: Findings and Implications

Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth comparative analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from two production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study their characteristics of load intensities, spatial patterns, and temporal patterns. We also compare the cloud block storage workloads with the notable public block-level I/O workloads from the enterprise data centers at Microsoft Research Cambridge, and identify the commonalities and differences of the three sources of traces. To this end, we provide 6 findings through the high-level analysis and 16 findings through the detailed analysis on load intensity, spatial patterns, and temporal patterns. We discuss the implications of our findings on load balancing, cache efficiency, and storage cluster management in cloud block storage systems.

cs.DC↗

arXiv2022

Asymptotic theory in network models with covariates and a growing number of node parameters

We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment estimation method using node degrees and homophily statistics. We establish consistency and asymptotic normality of our estimator using novel analysis. We apply our general framework to three applications, including both exponential family and non-exponential family models. Comprehensive numerical studies and a data example also demonstrate the usefulness of our method.

math.ST↗

arXiv2022

Efficient LSM-Tree Key-Value Data Management on Hybrid SSD/HDD Zoned Storage

Zoned storage devices, such as zoned namespace (ZNS) solid-state drives (SSDs) and host-managed shingled magnetic recording (HM-SMR) hard-disk drives (HDDs), expose interfaces for host-level applications to support fine-grained, high-performance storage management. Combining ZNS SSDs and HM-SMR HDDs into a unified hybrid storage system is a natural direction to scale zoned storage at low cost, yet how to effectively incorporate zoned storage awareness into hybrid storage is a non-trivial issue. We make a case for key-value (KV) stores based on log-structured merge trees (LSM-trees) as host-level applications, and present HHZS, a middleware system that bridges an LSM-tree KV store with hybrid zoned storage devices based on hints. HHZS leverages hints issued by the flushing, compaction, and caching operations of the LSM-tree KV store to manage KV objects in placement, migration, and caching in hybrid ZNS SSD and HM-SMR HDD zoned storage. Experiments show that our HHZS prototype, when running on real ZNS SSD and HM-SMR HDD devices, achieves the highest throughput compared with all baselines under various settings.

cs.PF↗

arXiv2022

Separating Data via Block Invalidation Time Inference for Write Amplification Reduction in Log-Structured Storage

Log-structured storage has been widely deployed in various domains of storage systems, yet its garbage collection incurs write amplification (WA) due to the rewrites of live data. We show that there exists an optimal data placement scheme that minimizes WA using the future knowledge of block invalidation time (BIT) of each written block, yet it is infeasible to realize in practice. We propose a novel data placement algorithm for reducing WA, SepBIT, that aims to infer the BITs of written blocks from storage workloads and separately place the blocks into groups with similar estimated BITs. We show via both mathematical and production trace analyses that SepBIT effectively infers the BITs by leveraging the write skewness property in practical storage workloads. Trace analysis and prototype experiments show that SepBIT reduces WA and improves I/O throughput, respectively, compared with state-of-the-art data placement schemes. SepBIT is currently deployed to support the log-structured block storage management at Alibaba Cloud.

cs.DC↗

arXiv2021

A network Poisson model for weighted directed networks with covariates

The edges in networks are not only binary, either present or absent, but also take weighted values in many scenarios (e.g., the number of emails between two users). The covariate-$p_0$ model has been proposed to model binary directed networks with the degree heterogeneity and covariates. However, it may cause information loss when it is applied in weighted networks. In this paper, we propose to use the Poisson distribution to model weighted directed networks, which admits the sparsity of networks, the degree heterogeneity and the homophily caused by covariates of nodes. We call it the \emph{network Poisson model}. The model contains a density parameter $μ$, a $2n$-dimensional node parameter $θ$ and a fixed dimensional regression coefficient $γ$ of covariates. Since the number of parameters increases with $n$, asymptotic theory is nonstandard. When the number $n$ of nodes goes to infinity, we establish the $\ell_\infty$-errors for the maximum likelihood estimators (MLEs), $\hatθ$ and $\hat{γ}$, which are $O_p( (\log n/n)^{1/2} )$ for $\hatθ$ and $O_p( \log n/n)$ for $\hat{γ}$, up to an additional factor. We also obtain the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings. ) for bθ and Op(log n/n) for bγ, up to an additional factor. We also obtain the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings.

math.ST↗

arXiv2021

A sparse $p_0$ model with covariates for directed networks

We are concerned here with unrestricted maximum likelihood estimation in a sparse $p_0$ model with covariates for directed networks. The model has a density parameter $ν$, a $2n$-dimensional node parameter $\bsη$ and a fixed dimensional regression coefficient $\bsγ$ of covariates. Previous studies focus on the restricted likelihood inference. When the number of nodes $n$ goes to infinity, we derive the $\ell_\infty$-error between the maximum likelihood estimator (MLE) $(\widehat{\bsη}, \widehat{\bsγ})$ and its true value $(\bsη, \bsγ)$. They are $O_p( (\log n/n)^{1/2} )$ for $\widehat{\bsη}$ and $O_p( \log n/n)$ for $\widehat{\bsγ}$, up to an additional factor. This explains the asymptotic bias phenomenon in the asymptotic normality of $\widehat{\bsγ}$ in \cite{Yan-Jiang-Fienberg-Leng2018}. Further, we derive the asymptotic normality of the MLE. Numerical studies and a data analysis demonstrate our theoretical findings.

math.ST↗

arXiv2020

Weighted directed networks with a differentially private bi-degree sequence

The $p_0$ model is an exponential random graph model for directed networks with the bi-degree sequence as the exclusively sufficient statistic. It captures the network feature of degree heterogeneity. The consistency and asymptotic normality of a differentially private estimator of the parameter in the private $p_0$ model has been established. However, the $p_0$ model only focuses on binary edges. In many realistic networks, edges could be weighted, taking a set of finite discrete values. In this paper, we further show that the moment estimators of the parameters based on the differentially private bi-degree sequence in the weighted $p_0$ model are consistent and asymptotically normal. Numerical studies demonstrate our theoretical findings.

math.ST↗

arXiv2019

Preliminary demonstration of flexible dual-energy X-ray phase-contrast imaging

Currently, dual-energy X-ray phase contrast imaging is usually conducted with an X-ray Talbot-Lau interferometer. However, in this system, the two adopted energy spectra have to be chosen carefully in order to match well with the phase grating. For example, the accelerating voltages of the X-ray tube are supposed to be respectively set as 40 kV and 70 kV, with other energy spectra being practically unusable for dual energy imaging. This system thus has low flexibility and maneuverability in practical applications. In this work, dual energy X-ray phase-contrast imaging is performed in a grating-based non-interferometric imaging system rather than in a Talbot-Lau interferometer. The advantage of this system is that, theoretically speaking, any two separated energy spectra can be utilized to perform dual energy X-ray phase-contrast imaging. The preliminary experimental results show that dual-energy X-ray phase contrast imaging is successfully performed when the accelerating voltages of the X-ray tube are successively set as 40 kV and 50 kV. Our work increases the flexibility and maneuverability when employing dual-energy X-ray phase-contrast imaging in medical diagnoses and nondestructive tests.

physics.med-ph↗

arXiv2013

Alignment of the photoelectron spectroscopy beamline at NSRL

The photoelectron spectroscopy beamline at National Synchrotron Radiation Laboratory (NSRL) is equipped with a spherical grating monochromator with the included angle of 174 deg. Three gratings with line density of 200, 700 and 1200 lines/mm are used to cover the energy region from 60 eV to 1000 eV. After several years operation, the spectral resolution and flux throughput were deteriorated, realignment is necessary to improve the performance. First, the wavelength scanning mechanism, the optical components position and the exit slit guide direction are aligned according to the design value. Second, the gratings are checked by Atomic Force Microscopy (AFM). And then the gas absorption spectrum is measured to optimize the focusing condition of the monochromator. The spectral resolving power is recovered to the designed value of 1000@244eV. The flux at the end station for the 200 lines/mm grating is about 10^10 photons/sec/200mA, which is in accordance with the design. The photon flux for the 700 lines/mm grating is about 5 X 10^8 photons/sec/200mA, which is lower than expected. This poor flux throughput may be caused by carbon contamination on the optical components. The 1200 lines/mm grating has roughness much higher than expected so the diffraction efficiency is too low to detect any signal. A new grating would be ordered. After the alignment, the beamline has significant performance improvements in both the resolving power and the flux throughput for 200 and 700 lines/mm gratings and is provided to users.

physics.ins-det↗