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152 results on '"Liu, Jingyu"'

32. Perception-Driven Hybrid Foveated Depth of Field Rendering for Head-Mounted Displays

Author

Liu, Jingyu, Mantel, Claire, Forchhammer, Søren, Liu, Jingyu, Mantel, Claire, and Forchhammer, Søren

Abstract

In this paper, we present a novel perception-driven hybrid rendering method leveraging the limitation of the human visual system (HVS). Features accounted in our model include: foveation from the visual acuity eccentricity (VAE), depth of field (DOF) from vergence & accommodation, and longitudinal chromatic aberration (LCA) from color vision. To allocate computational workload efficiently, first we apply a gaze-contingent geometry simplification. Then we convert the coordinates from screen space to polar space with a scaling strategy coherent with VAE. Upon that, we apply a stochastic samplingbased on DOF. Finally, we post-process the Bokeh for DOF, which can at the same time achieve LCA and anti-aliasing. A virtual reality (VR) experiment on 6 Unity scenes with a head-mounted display (HMD) HTC VIVE Pro Eye yields frame rates range from 25.2 to 48.7 fps. Objective evaluation with FovVideoVDP - a perceptual based visible difference metric - suggests that the proposed method gives satisfactory just-objectionable-difference (JOD) scores across 6 scenes from 7.61 to 8.69 (in a 10 unit scheme). Our method achieves better performance compared with the existing methods while having the same or better level of quality scores.

Published
2021

33. Who Is Briliant? The Financial Competitiveness of Listed Companies from Rustbelt and Sunbelt in China

Author

Liu Jingyu, Wei Chentao, and Zhao Yanming

Subjects
Finance, Financial management, Index system, business.industry, Profitability index, business, China
Abstract

Based on the financial data in 2018, of 43 listed companies in Liaoning Province and 49 relevant listed companioes in Jiangsu Province, the paper applies SPSS software to construct a financial competitiveness index system including 14 financial indicators, and uses factor analysis to analyze the four ability dimensions of profitability, operational capacity, solvence and development ability, to evaluate the financial competitiveness of the selected companies in two Provinces. The results show that the general financial competitiveness of listed companies in Liaoning Province is lower and the measures to improve the financial competitiveness are given for the listed companies in Liaoning Province. This paper is an interdisciplinary research of data statistics and financial management.

Published
2020

34. ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction.

Author

Xu, Jiaxi, Ai, Jun, Liu, Jingyu, and Shi, Tao

Subjects
SYSTEMS software, COMPUTER software quality control, FORECASTING, PREDICTION models
Abstract

Recognizing and repairing defects to enhance quality in software life circle has become a critical research topic. Unfortunately, it is difficult to guarantee the validity of the defect prediction method based on manually designed features proposed in previous studies. Numerous scholars have endeavored to use a single model to obtain prediction results for different types of fault, but this is difficult to perform. This article improves the defect representation and prediction model in software defect prediction, proposing Augmented-Code Property Graph (CPG) based defect prediction method (ACGDP). Augmented-CPG is a novel encoding graph format introduced in this article. Based on Augmented-CPG, we suggested defect region candidate extraction approach linked to the defect category. Graph neural networks are used for obtaining defect characteristics. Experiments on three distinct types of defects indicate that ACGDP can predict certain classed of defects effectively. [ABSTRACT FROM AUTHOR]

Published
2022
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44. Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI Identification.

Author

Li, Yang, Liu, Jingyu, Jiang, Yiqiao, Liu, Yu, and Lei, Baiying

Subjects
*CONVOLUTIONAL neural networks, *LARGE-scale brain networks, *MILD cognitive impairment, *GRAPH connectivity, *KALMAN filtering
Abstract

Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals macroscopic dynamic neural activity patterns for brain disease identification. However, dFC methods ignore the causal influence between the brain regions. Furthermore, due to the complex non-Euclidean structure of brain networks, advanced deep neural networks are difficult to be applied for learning high-dimensional representations from brain networks. In this paper, a group constrained Kalman filter (gKF) algorithm is proposed to construct dynamic effective connectivity (dEC), where the gKF provides a more comprehensive understanding of the directional interaction within the dynamic brain networks than the dFC methods. Then, a novel virtual adversarial training convolutional neural network (VAT-CNN) is employed to extract the local features of dEC. The VAT strategy improves the robustness of the model to adversarial perturbations, and therefore avoids the overfitting problem effectively. Finally, we propose the high-order connectivity weight-guided graph attention networks (cwGAT) to aggregate features of dEC. By injecting the weight information of high-order connectivity into the attention mechanism, the cwGAT provides more effective high-level feature representations than the conventional GAT. The high-level features generated from the cwGAT are applied for binary classification and multiclass classification tasks of mild cognitive impairment (MCI). Experimental results indicate that the proposed framework achieves the classification accuracy of 90.9%, 89.8%, and 82.7% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification respectively, outperforming the state-of-the-art methods significantly. [ABSTRACT FROM AUTHOR]

Published
2022
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45. Effects of Irradiation on Surface Charge and Discharge Behaviors of PI Film Under Pulse Voltage.

Author

Cao, Jianmei, Li, Jie, Liu, Jingyu, Zheng, Jingquan, Xing, Yunqi, and Wang, Yao

Subjects
SURFACE charging, BREAKDOWN voltage, SUPERCONDUCTING magnets, POWER resources, PARTIAL discharges, SURFACE charges, POLYIMIDES, ELECTRIC discharges
Abstract

The superconducting magnet, as a main component of the international thermonuclear experimental reactor (ITER), is critical to the ITER's long-term stability. Polyimide (PI) not only has excellent electrical properties at cryogenic temperature, but also has strong irradiation resistance, which has become the common choice of insulating materials for the superconducting magnet. Due to the pulsed power supply used in the ITER, PI is facing the dual challenges of cryogenic temperature and pulsed voltage. The surface charge characteristics of irradiated PI film by different doses of electron beam were studied in this paper. The surface charging is completed under the pulse voltage with 40% relative humidity. The partial discharge initial voltage (PDIV) and breakdown voltage in liquid nitrogen (LN2) were measured by electrostatic voltmeter. The findings indicate that proper irradiation can help to minimize charge accumulation and decrease the trap level. At the same time, the PDIV and breakdown voltage can be increased by irradiation. However, too much irradiation can weaken this influence. [ABSTRACT FROM AUTHOR]

Published
2021
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46. A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation.

Author

Lian, Jie, Liu, Jingyu, Zhang, Shu, Gao, Kai, Liu, Xiaoqing, Zhang, Dingwen, and Yu, Yizhou

Subjects
*X-ray imaging, *X-rays, *BASE pairs, *RADIOSCOPIC diagnosis, *IMAGE segmentation, *FEATURE extraction
Abstract

Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation module propagating co-occurrence and causal relations into disease proposals. Towards making a practical system, we also provide ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. We evaluate our SAR-Net on it and another dataset DR-Private. Experimental results show that it can enhance the strong baseline of Mask R-CNN with significant improvements. The ChestX-Det is released at https://github.com/Deepwise-AILab/ChestX-Det-Dataset. [ABSTRACT FROM AUTHOR]

Published
2021
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