Xinpeng Li

Case Western Reserve University

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i-10 index

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Author Order

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Co-Authors

Mingyan Han

Megvii (China)

Haoqiang Fan

Megvii (China)

Shuaicheng Liu

Megvii (China)

Vipin Chaudhary

Case Western Reserve University

Qi Wu

Shanghai Jiao Tong University

Qiang Guan

Kent State University

Chengzhi Jiang

Megvii (China)

Vinooth Kulkarni

Case Western Reserve University

Ting Jiang

Hunan Institute of Science and Technology

Radu Timofte

ETH Zurich

Cooperative Institutions

University of Würzburg

Chinese Academy of Sciences

Huawei Technologies (Sweden)

University of Southern Denmark

Shanghai Jiao Tong University

University of Science and Technology of China

Harbin Institute of Technology

University of Electronic Science and Technology of China

Beijing Institute of Technology

University of Chinese Academy of Sciences

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A Parallel Host Log Analysis Approach Based on Spark

2021 17th International Conference on Computational Intelligence and Security (CIS) (2018)

Xinpeng Li Yong Wang Hao Feng Wenlong Ke

Intrusion detection plays a key role in maintaining the security of computer networks. Host-based intrusion detection systems usually analyze log data to discover host abnormal behavior. In recent years, with the rapid growth of massive host log data generated by virtual machines in the cloud environment, the traditional log analysis methods are limited by factors such as single data source, independent data, large data volume, and insufficient single-point computing capability. To solve this problem, this paper proposes a Spark-based host log data processing method, which first expands the data dimension based on Spark SQL to obtain more detailed dimensional data; then accomplish the query (especially union query) and counting complex data for more comprehensive host health used Spark SQL. Series of experiments result show that our proposed method can achieve platform scalability and has well time performance in log data processing.

SPARK (programming language)

10.1109/cis2018.2018.00073

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Citations (3)

Research on Hierarchical P2P Based RFID Code Resolution Network and Its Security

Wen Zhao Xueyang Liu Xinpeng Li Dianxing Liu Shikun Zhang

In order to build large-scale RFID applications, it is necessary to establish public RFID service infrastructure, the essential task is to build efficient and secure RFID code resolution network. Aiming at alleviating the problems in load balancing, single node failure tolerance and security of resolution network, based on related standards and research works, a hierarchical P2P based RFID code resolution network structure is proposed, k-Nearest Neighbor storing and hierarchical querying mechanism are presented in static resolution, secondary indexing and hierarchical querying mechanism are presented in dynamic resolution, double random number based entity and message authentication mechanism is given for the correctness of resolution service. Based on modified Kademlia, RFID code resolution services and related security mechanism are implemented. In experiments, the availability, load balancing and single node failure tolerance of our resolution network are verified.

Code (set theory)

10.1109/fcst.2009.12

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Citations (5)

Efficient Quantum Circuit Cutting by Neglecting Basis Elements

arXiv (Cornell University) (2023)

Daniel T. Chen Ethan H. Hansen Xinpeng Li Vinooth Kulkarni Vipin Chaudhary

Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through scales exponentially. We propose a simple reduction scheme to lower the classical and quantum resources required to perform a cut. Particularly, we recognize that for some cuts, certain basis element might pass "no information" through the qubit wire and can effectively be neglected. We empirically demonstrate our method on circuit simulators as well as IBM quantum hardware, and we observed up to 33 percent reduction in wall time without loss of accuracy.

Basis (linear algebra)

Quantum circuit

10.48550/arxiv.2304.04093

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Citations (1)

DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution

arXiv (Cornell University) (2023)

Lei Yu Xinpeng Li Youwei Li Ting Jiang Qi Wu

Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly focused on reducing the number of parameters and floating-point operations through various network designs. Although these methods can decrease the number of parameters and floating-point operations, they may not necessarily reduce actual running time. To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance. Specifically, we leverage enhanced high-resolution output as additional supervision to improve the learning ability of lightweight student networks. Upon convergence of the student network, we further simplify our network structure to a more lightweight level using reparameterization techniques and iterative network pruning. Meanwhile, we adopt an effective lightweight network training strategy that combines multi-anchor distillation and progressive learning, enabling the lightweight network to achieve outstanding performance. Ultimately, our proposed method achieves the fastest inference time among all participants in the NTIRE 2023 efficient super-resolution challenge while maintaining competitive super-resolution performance. Additionally, extensive experiments are conducted to demonstrate the effectiveness of the proposed components. The results show that our approach achieves comparable performance in representative dataset DIV2K, both qualitatively and quantitatively, with faster inference and fewer number of network parameters.

Leverage (statistics)

Boosting

Pruning

10.48550/arxiv.2304.07018

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Research on Operation Characteristics of Distributed PV based on Measured Data

2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) (2021)

Shoubin Diao Xinpeng Li Xin Cao Jian Li Peixiang Du

With the increasing penetration of distributed photovoltaic(PV), the fluctuation of high-permeability PV will have a series of impacts on the power grid. The analysis of power quality characteristics at the voltage levels of PV low-voltage grid-connected point (380V) and PV concentrated grid-connected point (10kV) is the basis for the study of PV grid-connected related problems. First, the principle of distributed PV power generation, the principle and structure of grid-connected system, and the influence of grid-connected distributed PV power generation on power grid system are analyzed. Then, based on the measured data of single household distributed PV power generation, the output and operation characteristics of distributed PV power generation are studied. At last, combined with the measured data of PV power generation grid, the multi-index power quality problem of PV power generation grid is analyzed theoretically and experimentally.

Distributed power

Stand-alone power system

10.1109/precede51386.2021.9680899

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A Message-based Software Architecture Style for Distributed Application

Guozhen Tan Xinpeng Li Jiankun Wu Hongzhuo Zhao Chengxu Li

Software Architecture (SA) is emerging as an important research area in software engineering and forms the backbone for building successful softwareintensive systems. Based on practical requirements, especially the requirements of distributed applications and Internet applications, a new architectural style referred to as TreeSA is proposed. The primary elements of TreeSA and he message routing mechanism are discussed. Finally, an application based on TreeSA is outlined.

Architectural style

Software architecture description

Resource-oriented architecture

10.1109/pdcat.2005.19

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Citations (2)

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

arXiv (Cornell University) (2021)

Zhen Liu Wenjie Lin Xinpeng Li Qing Rao Ting Jiang

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-$l$ of 39.4471 and a PSNR-$\mu$ of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.

Pyramid (geometry)

High-dynamic-range imaging

Frame rate

10.48550/arxiv.2105.10697

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Citations (1)

MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2023)

Yuekun Dai Chongyi Li Shangchen Zhou Ruicheng Feng Qingpeng Zhu

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge including four tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2023. In total, 120 participants were successfully registered, and 11 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.

Flare

10.1109/cvprw59228.2023.00286

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Citations (6)

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2022)

Eduardo Pérez-Pellitero Sibi Catley-Chandar Richard A. Shaw Aleš Leonardis Radu Timofte

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

Ground truth

High fidelity

High-dynamic-range imaging

Baseline (sea)

10.1109/cvprw56347.2022.00114

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Citations (31)

Accelerating VQE Algorithm via Parameters and Measurement Reuse

Xinpeng Li Ji Liu Ethan H. Hansen Shuai Xu Paul Hovland

The Variational Quantum Eigensolver (VQE), a quantum-classical hybrid algorithm, holds significant potential for advancements in quantum chemistry and optimization tasks. Our study focuses on accelerating the VQE in combinatorial optimization tasks such as Max-Cut, k-Clique, and k-SAT, while addressing the challenges of scalability and measurement overhead in quantum devices. Notably, while solving one problem, it allows us to concurrently compute essential information like expectation values and gradients for multiple other problems. This capability is made feasible because combinatorial optimization problems often share the same measurement basis. Drawing inspiration from this insight, we introduce a method that efficiently solves multiple problems by appropriately selecting their initial points. Subsequently, we propose two selection strategies for the method. Employing these strategies can, to some extent, help avoid the issue of becoming trapped in local optima, thus leading to improved accuracy. Our tests on 5-node and 10-node graphs for Max-Cut and k-Clique show that, in some cases, our method outperforms random initialization in terms of both accuracy and number of iterations.

Initialization

Clique

Clique problem

10.1109/icrc60800.2023.10386370

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